Alternatives to LiveDesign

Revolutionize the fields of drug discovery and materials research through cutting-edge molecular modeling techniques. Our computational platform, grounded in physics, combines unique solutions for predictive modeling, data analysis, and collaboration, facilitating swift navigation of chemical space. This innovative platform is employed by leading industries globally, serving both drug discovery initiatives and materials science applications across various sectors including aerospace, energy, semiconductors, and electronic displays. It drives our internal drug discovery projects, overseeing processes from target identification through hit discovery and lead optimization. Additionally, it enhances our collaborative research efforts aimed at creating groundbreaking medicines to address significant public health challenges. With a dedicated team of over 150 Ph.D. scientists, we commit substantial resources to research and development. Our contributions to the scientific community include more than 400 peer-reviewed publications that validate the efficacy of our physics-based methodologies, and we remain at the forefront of advancing computational modeling techniques. We are steadfast in our mission to innovate and expand the possibilities within our field.

Eidogen-Sertanty's Target Informatics Platform (TIP) stands out as the pioneering structural informatics system and knowledgebase that empowers researchers to explore the druggable genome through a structural lens. By harnessing the burgeoning wealth of experimental protein structure data, TIP revolutionizes structure-based drug discovery, shifting it from a limited, low-throughput field to a dynamic and data-rich scientific discipline. It is specifically designed to connect the realms of bioinformatics and cheminformatics, providing drug discovery scientists with a repository of insights that are not only unique but also highly synergistic with the information available from traditional bio- and cheminformatics tools. The platform's innovative combination of structural data management with advanced target-to-lead calculation and analytical capabilities significantly enhances every phase of the drug discovery process. With TIP, researchers are better equipped to navigate the complexities of drug development and make informed decisions.

Founded in 2000, VeraChem LLC aims to enhance the field of computer-aided drug discovery and molecular design by creating advanced computational chemistry techniques that merge innovative basic science with practical applications in research. A key aspect of the company's strategy for product development lies in delivering efficient, high-performance software solutions along with extensive user support. Among the current capabilities of VeraChem's software are predictions for protein-ligand and host-guest binding affinities, rapid and precise calculations of partial atomic charges for drug-like molecules, and the computation of energies and forces utilizing widely-used empirical force fields. Additionally, the software features automatic generation of alternate resonance forms for drug-like compounds, a robust conformational search enabled by the Tork algorithm, and the automatic identification of topological and three-dimensional molecular symmetries. The modular code base of VeraChem’s software packages allows for flexibility and adaptability in meeting diverse research needs, ensuring that users can leverage these tools effectively for their specific applications.

An integrated platform that bridges scientific reasoning, compound development, and computational tools is now available. Chemaxon’s Design Hub supports medicinal chemistry by facilitating the analysis and prioritization of innovative ideas. This unified platform allows users to design compounds and manage concepts seamlessly. Transitioning from traditional PowerPoint presentations to dynamic, visually engaging, and chemically searchable hypotheses enhances the compound design workflow. Users can conveniently utilize established physicochemical properties, computational models, novelty considerations, or accessible compound catalogs within an interactive visual setting. Collaborate with your Contract Research Organizations (CROs) in the compound development journey through this secure online resource. Furthermore, assess the gathered data from biological assays or experimental structural insights to derive Structure-Activity Relationships (SAR) and generate fresh hypotheses for subsequent optimization cycles. Your scientific ideas can be conveniently organized in a “designer's electronic lab notebook,” which features chemically aware drawing tools that streamline the design process. This comprehensive approach ensures that every step of compound development is efficient and well-documented, paving the way for future innovations.

BIOiSIMTM represents a groundbreaking 'virtual drug development engine' that significantly enhances the drug development sector by effectively identifying drug compounds that are most likely to provide meaningful therapeutic benefits for various diseases or conditions. We provide an array of translational solutions that are tailored to meet the specific needs of your pre-clinical and clinical initiatives. Central to our offerings is the highly validated BIOiSIMTM platform, which supports the development of small molecules, large molecules, and viruses. This innovative platform is underpinned by extensive data derived from thousands of compounds across seven different species, resulting in a level of robustness that is uncommon in the field. Emphasizing human health outcomes, the heart of the platform features a translatability engine that seamlessly converts insights gained from different species. Importantly, the BIOiSIMTM platform can be deployed prior to the initiation of preclinical animal trials, facilitating earlier insights and potentially reducing the costs associated with outsourced experimentation. By integrating these advanced capabilities, we aim to streamline the drug development process and accelerate the journey from discovery to market.

Makya stands out as the pioneering user-centric SaaS platform dedicated to AI-enhanced de novo drug design, particularly emphasizing Multi-Parametric Optimization (MPO). This innovative tool empowers users to create novel and easily synthesize compounds based on a multi-objective framework, achieving unprecedented levels of speed, efficiency, and variety. Makya incorporates a range of generative algorithms tailored to various stages of drug development, from hit discovery to lead optimization; it includes a fine-tuning generator for pinpointing ideal solutions within your specified chemical landscape, a novelty generator designed to explore fresh concepts for re-scaffolding and hit discovery, and a forward generator to create a targeted library of compounds that can be readily synthesized from commercially available starting materials. The recently introduced Makya 3D module significantly improves both the user interface and the scientific capabilities of the platform. With a comprehensive array of 3D modeling functionalities available for both ligand-based and structure-based approaches, Makya 3D allows for the calculation of 3D scores, which can be seamlessly utilized to guide compound generation within the platform. This integration not only enhances the design process but also offers researchers deeper insights into their molecular designs.

AI-powered drug discovery is a proven technology. Cerella extracts hidden insights from your drug discovery data to reveal the best compounds and most valuable experiment for your project. It can make confident predictions by accurately filling in the missing values. This is especially useful for expensive downstream experiments, which are impossible to predict using other methods. This allows you to do more with sparse and limited data sets.

3decision® serves as a cloud-based repository for protein structures, focusing on efficient management of structural data and offering sophisticated analytics to support teams involved in the discovery of small molecules and biologics, thereby expediting the process of structure-based drug design. The platform consolidates and standardizes both experimental and computational protein structures sourced from publicly available databases such as RCSB PDB and AlphaFoldDB, in addition to proprietary datasets, and accommodates formats like PDBx/mmCIF and ModelCIF. This comprehensive approach guarantees seamless access to a variety of structural formats including X-Ray, NMR, cryo-EM, and modeled structures, thereby promoting collaboration and bolstering research initiatives. In addition to its storage capabilities, 3decision® enhances each entry with valuable metadata and sequence information, which encompasses details on protein-ligand interactions, antibody annotations, and specifics about binding sites. Equipped with advanced analytical instruments, the platform is capable of pinpointing druggable sites, evaluating off-target risks, and facilitating comparisons of binding sites, which collectively transform extensive structural datasets into practical insights that can drive research forward. Furthermore, its cloud-based architecture fosters enhanced collaboration among research teams, making it easier for scientists to share findings and insights, ultimately leading to more innovative approaches in drug discovery and development.

We combine clinical and experimental data through machine learning techniques to explore human disease biology and promote the development of precision medicine. Our innovative Contingent AI™ technology comprehends the intricate relationships present in the data, yielding advanced insights. To combat data bias, we refine our machine learning models based on decisions made during the pre-processing and feature engineering phases. We utilize zebrafish, cellular, and various phenotypic animal models to test and confirm in silico predictions through in vivo experiments, along with genetic modifications conducted both in vitro and in vivo to enhance translation. By employing active learning and computer vision on validated models that focus on cardiac, central nervous system, and rare disorders, we swiftly integrate new data into our machine learning frameworks, allowing for continuous improvement and adaptation in our methodologies. This iterative process not only enhances the accuracy of our predictions but also enables us to stay at the forefront of research in precision medicine.

StarDrop™, a comprehensive suite of integrated software, delivers the best in silico technology within a highly visual interface. StarDrop™, which allows seamless flow between the latest data, predictive modeling, and decision-making regarding the next round or synthesis, improves the speed, efficiency and productivity of the discovery process. A balance of different properties is essential for successful compounds. StarDrop™, which guides you through the multi-parameter optimization challenge, helps you target compounds with the highest chance of success. It also saves you time and resources by allowing you to synthesize fewer compounds and test them less often.

Harnessing RNA sequencing (RNA-seq) data alongside Artificial Intelligence presents both a crucial necessity and a significant opportunity for creating therapies aimed at correcting splicing errors. By leveraging machine learning, we can uncover novel splicing errors and swiftly formulate therapeutic compounds to address them. Our AI platform, SpliceCore, is specifically designed for discovering RNA therapeutics. This cutting-edge technology focuses on analyzing RNA sequencing data with unparalleled efficiency. It can swiftly identify, evaluate, and validate potential drug targets, outpacing traditional methodologies. Central to SpliceCore is our unique repository containing over 5 million potential RNA splicing errors, making it the largest of its kind globally and instrumental for testing any RNA sequencing dataset submitted for analysis. The integration of scalable cloud computing allows us to handle vast quantities of RNA sequencing data in a way that is not only efficient but also cost-effective, significantly speeding up the pace of therapeutic advancements. This innovative approach promises to revolutionize the landscape of RNA therapeutics.

Our innovative AI engine is revolutionizing the drug discovery process, enabling the creation of superior medications at an accelerated pace. The breakthroughs we achieve contribute to the development of medicines more efficiently and effectively. Our portfolio of AI-driven discoveries encompasses entirely owned and collaboratively developed pipeline assets, supported by leading investors in the industry. Atomwise has engineered a cutting-edge machine-learning discovery platform that merges the capabilities of convolutional neural networks with extensive chemical libraries to identify new small-molecule treatments. The key to transforming drug discovery through AI lies in our talented team. We are committed to enhancing our AI platform and leveraging it to revolutionize the discovery of small molecule drugs. It is essential that we confront the most daunting and seemingly insurmountable targets, streamlining the entire drug discovery process to provide developers with increased opportunities for success. Enhanced computational efficiency allows us to screen trillions of compounds virtually, significantly boosting the chances of finding viable solutions. Our impressive model accuracy has successfully addressed the persistent issue of false positives, underscoring the reliability of our approach. Ultimately, our dedication to innovation and excellence sets us apart in the quest for breakthrough therapies.

alvaMolecule serves as a no-code cheminformatics platform designed to visualize, curate, and standardize molecular datasets in preparation for analysis. It accommodates popular molecular formats, including SMILES and SDF/MOL2, allowing users to navigate through collections in either grid or spreadsheet formats, with automatic import of relevant data. This tool ensures structure verification and standardization via pre-set standardizers and customizable SMIRKS rules, facilitates the identification and management of duplicates, and provides scaffold analysis for summarizing fundamental frameworks. Additionally, it features integrated filters and charting options that allow sorting based on substructures, calculated molecular descriptors, and physicochemical properties. alvaMolecule is capable of calculating around 88 structural and physicochemical properties, which encompass drug-like and lead-like scores such as LogP, TPSA, and the Lipinski alert index, ultimately assisting users in generating high-quality datasets for QSAR/QSPR modeling, descriptor calculations, and virtual screening processes. Furthermore, its user-friendly interface ensures that researchers, regardless of their coding expertise, can easily navigate and utilize the tool to enhance their cheminformatics tasks effectively.

CDD Vault allows you to intuitively organize chemical structures, biological study data, as well as collaborate with external or internal partners via a simple web interface. Start a free trial to see how easy it can be to manage drug discovery data. Tailored for You Affordable Scales with your project team Activity & Registration * Electronic Lab Notebook * Visualization * Inventory * APIs

AQBioSim is an innovative cloud-based platform created by SandboxAQ that utilizes Large Quantitative Models (LQMs) based on principles of physics and chemistry to transform the processes of material discovery and optimization. By combining techniques such as Density Functional Theory (DFT), Iterative Full Configuration Interaction (iFCI), Generative AI, Bayesian Optimization, and Chemical Foundation Models, AQBioSim facilitates highly accurate simulations of molecular and material behaviors in real-world scenarios. Among its numerous features, AQBioSim can predict performance under various stressors, enhance formulation processes through in silico testing, and investigate eco-friendly chemical methods. A standout achievement of AQBioSim lies in its remarkable progress in battery technology, where it has cut the time needed for lithium-ion battery end-of-life predictions by an astonishing 95%, while also attaining 35 times greater accuracy using only 50 times less data. This platform thus not only accelerates material innovation but also significantly contributes to advancements in sustainable energy solutions.

Uncover biological markers, generate insights into the mechanisms of disease, identify novel pharmaceuticals, or detect variations in protein concentrations through a meticulously structured series of experiments. We have simplified the process of harnessing the potential of mass spectrometry and proteomics, enabling you to concentrate on the intricacies of biology and advance toward groundbreaking discoveries. Our automated and consistent workflow facilitates faster initiation and completion of experiments, granting you the authority and adaptability to make timely decisions. By prioritizing biological insights and fostering collaborative efforts, our scalable proteomics data processing system is designed for repeated use. We have delegated intensive and repetitive tasks to the cloud, ensuring a smooth and satisfying experience. Our sophisticated proteomics workflow effectively integrates numerous complex elements, allowing for the efficient analysis and processing of larger-scale experiments, ultimately enhancing the research journey. Thus, with our innovative approach, researchers can now delve deeper into the molecular landscape and achieve more significant breakthroughs than ever before.

As a member of the DeepMatter Group, we persist in providing our advanced platforms for Synthesis & Reaction Prediction, Information Extraction, Cheminformatics, and the groundbreaking DigitalGlassware®, which is a cloud-based digital chemistry solution from DeepMatter™ that enhances recordability, reproducibility, and shareability throughout every phase of the discovery journey, from the initial planning of reactions to the final analysis of results. Our collaboration with clients and users remains strong as we develop innovative software solutions designed to elevate chemical research and refine scientific workflows. DeepMatter boasts a diverse range of products that significantly speed up and enhance the processes of hypothesis generation, design, and synthesis. By utilizing these tools, new compounds—including pharmaceuticals, agrichemicals, and performance chemicals—can reach the market more swiftly. Additionally, DigitalGlassware converts your chemistry into digital code, ultimately boosting laboratory productivity and efficiency in research endeavors. This fusion of technology and chemistry opens up new avenues for innovation and collaboration in the scientific community.

Genedata Biologics® enhances the development of biotherapeutics, including bispecifics, ADCs, TCRs, CAR-Ts, and AAVs, providing a comprehensive solution for the industry. Recognized as the leading platform in the field, it seamlessly unifies all discovery workflows, allowing researchers to prioritize genuine innovation. By utilizing a pioneering platform that was purposefully created to digitalize the biotherapeutic discovery process, research can be accelerated significantly. The platform simplifies intricate R&D tasks by facilitating the design, tracking, testing, and evaluation of novel biotherapeutic drugs. It is compatible with various formats, such as antibodies, bi- or multi-specifics, ADCs, innovative scaffolds, and therapeutic proteins, as well as engineered therapeutic cell lines like TCRs and CAR-T cells. Functioning as a comprehensive end-to-end data backbone, Genedata Biologics connects all R&D processes, including library design, immunization, selection and panning, molecular biology, screening, protein engineering, expression, purification, and protein analytics, ultimately leading to thorough assessments of candidate developability and manufacturability. This holistic integration ensures that researchers can make informed decisions and push the boundaries of biotherapeutic innovation effectively.

Proteins, which are remarkably complex machines, play a crucial role not only in the biological functions of your body but also in every living organism's processes. They serve as the fundamental units of life. As of now, there are approximately 100 million identified proteins, with discoveries being made regularly. Each protein possesses a distinctive three-dimensional shape that is essential to its functionality and purpose. However, determining a protein's precise structure is often a costly and lengthy endeavor, resulting in an understanding of only a small percentage of the proteins recognized by science. Addressing this growing disparity and developing methods to predict the structures of millions of yet-to-be-discovered proteins could significantly advance our ability to combat diseases, expedite the discovery of new treatments, and potentially unveil the secrets of life's mechanisms. The implications of such advancements could transform both medicine and our understanding of biology.

DNAnexus Apollo™ enhances the efficiency of precision drug discovery by fostering collaboration that extracts valuable insights from omics data. The process of precision drug discovery involves the aggregation and examination of vast amounts of omics and clinical information. These extensive datasets serve as valuable assets; however, many traditional and custom-built informatics tools struggle to manage their intricacies and scale. Additionally, the effectiveness of precision medicine initiatives can be hindered by fragmented data sources, inadequate collaboration tools, and the challenges posed by complex, evolving regulatory and security demands. By enabling scientists and clinicians to jointly investigate and interpret omics and clinical data within a unified framework, DNAnexus Apollo™ bolsters precision drug discovery efforts. This platform, which is powered by a resilient and scalable cloud infrastructure, facilitates the seamless and secure sharing of data, tools, and analyses among peers and collaborators, regardless of whether they are nearby or across the globe. Ultimately, Apollo not only streamlines the data-sharing process but also enhances the overall collaborative experience in the pursuit of innovative drug discoveries.

alvaBuilder is an innovative molecular design software that facilitates the creation of new chemical structures tailored to specific user-defined criteria, including structural, physicochemical, and modeling parameters. This tool allows for the generation of entirely new molecules from the ground up or the modification of existing ones through fragment-based and rule-driven methodologies. Moreover, alvaBuilder harmonizes with QSAR/QSPR workflows, empowering users to influence the molecular generation process through predictive models, ranges of descriptors, and targeted properties. This software is particularly beneficial for medicinal chemistry, lead optimization, and virtual screening endeavors, efficiently navigating chemical space while ensuring both chemical viability and interpretability. Designed for both research and industrial purposes, alvaBuilder is an essential resource for scenarios requiring molecular generation that is transparent, controllable, and reproducible, making it a valuable asset in the field of drug discovery. By providing these capabilities, it enhances the potential for innovative solutions in chemical research and development.

The biopharmaceutical sector today is characterized by its intricacy, driven by increasing demands for enhanced specificity and safety, the emergence of new treatment classes, and the complexity of disease mechanisms. To navigate this intricate landscape, a profound comprehension of therapeutic dynamics is essential. Advanced modeling and simulation techniques offer a distinctive approach to investigate biological and physicochemical phenomena at the atomic scale. This methodology not only informs physical experimentation but also expedites the drug discovery and development phases. BIOVIA Discovery Studio integrates more than three decades of peer-reviewed research with cutting-edge in silico methodologies, including molecular mechanics, free energy assessments, and biotherapeutics developability, all within a unified framework. By equipping researchers with a comprehensive suite of tools, it facilitates a deeper examination of protein chemistry, thereby accelerating the discovery of both small and large molecule therapeutics, from Target Identification all the way through to Lead Optimization. Ultimately, this synergy of research and technology underscores the vital role of innovative tools in transforming biopharmaceutical advancements.

Reaxys is an online resource created by Elsevier that enables users to access a wealth of information regarding chemical substances and data sourced from published academic literature, including both journals and patents. This platform facilitates the retrieval of details about chemical compounds, reactions, properties, along with bibliographic and substance data, which includes synthesis planning guidance and experimental methods derived from a curated selection of journals and patents. Introduced in 2009 as a modern alternative to the CrossFire databases, Reaxys was crafted to provide research chemists with both contemporary and historical insights into organic, inorganic, and organometallic chemistry, all via a user-friendly interface. Spanning over two centuries of chemical research, it draws information from thousands of journal articles, books, and patents, ensuring a comprehensive resource. The database emphasizes data derived from specific journals and chemistry patents, prioritizing entries that feature a chemical structure, are validated by experimental data, and contain reliable citations, thus enhancing the credibility of the information provided. Furthermore, Reaxys continues to evolve, ensuring that it meets the ongoing needs of researchers in the ever-changing landscape of chemical research.

The process of introducing a new medication to the market, starting from initial development to the final launch, is both time-intensive and heavily regulated, often spanning over a decade or more. Achieving success in this endeavor hinges on the timely availability of precise analytical data, which is essential for making informed decisions during the early stages of development and reducing the likelihood of setbacks later on. Modern drug development primarily follows a systematic approach, with the crucial first step usually being the identification of a biological target to concentrate efforts on. This target identification demands a comprehensive understanding of the characteristics of the candidates, enabling swift and reliable identification of the most promising options. After establishing a biological target, the next significant hurdle is identifying the most advantageous lead molecules, which entails discovering potential drug candidates—these may include small organic compounds or biological constructs with therapeutic capabilities. Thus, the entire journey from concept to market is a complex interplay of scientific insight and strategic decision-making.

Site-Identification by Ligand Competitive Saturation (SILCS) produces three-dimensional maps, known as FragMaps, that illustrate how different chemical functional groups interact with a specific target molecule. By revealing the complexities of molecular dynamics, SILCS offers tools that enhance the optimization of ligand scaffolds through both qualitative and quantitative insights into binding pockets, thereby streamlining the drug design process. This approach employs a range of small molecule probes, each featuring diverse functional groups, alongside explicit solvent modeling and accommodating the flexibility of the target molecule to effectively map protein targets. Furthermore, the technique allows researchers to visualize advantageous interactions with the target macromolecule. With these insights, scientists can strategically design improved ligands with functional groups situated in optimal positions for enhanced efficacy. The innovative nature of SILCS represents a significant advancement in the field of medicinal chemistry.

alvaDesc is a cheminformatics tool designed for the computation and examination of molecular descriptors, fingerprints, and structural patterns, catering to QSAR, QSPR, read-across, and machine learning needs. It is capable of calculating over 5,000 molecular descriptors across various dimensions (0D–3D), which encompass constitutional, topological, geometrical, electronic, physicochemical, and fragment-based categories. In addition, the software produces molecular fingerprints and structural pattern counts that facilitate similarity analysis, clustering, and classification tasks. It comes equipped with integrated tools that allow for descriptor filtering and correlation analysis, ensuring that the modeling process is both robust and reproducible. Furthermore, alvaDesc offers seamless integration with KNIME and Python, making it easy to link with external data analysis and machine learning workflows. Its widespread use in both academic and industrial research is bolstered by comprehensive documentation and an array of scientific publications, which contribute to its reputation as a reliable resource in the field. Moreover, users appreciate its user-friendly interface that enhances the overall experience while conducting complex cheminformatics tasks.

Harness the capabilities of artificial intelligence to accelerate the transition from laboratory research and experimental findings to the introduction of transformative therapies. Achieve a remarkable increase in research efficiency, potentially improving productivity by as much as 90% by cutting down your literature review time from several months to mere minutes. Eliminate distractions and enhance your search capabilities with a precise and accurate tool that simplifies the navigation of the expanding landscape of scientific publications. This approach not only saves time but also minimizes bias and enhances the likelihood of discovering groundbreaking insights. Delve deeply into the intricacies of disease biology and engage in sophisticated target identification. Causaly's advanced knowledge graph integrates data from countless publications, enabling thorough and objective scientific investigations. Effortlessly explore the intricate biological cause-and-effect dynamics without requiring extensive expertise. Access a comprehensive array of scientific documents and reveal previously overlooked connections. Causaly’s robust AI system processes millions of biomedical articles, facilitating improved decision-making and enhancing research outcomes, ultimately leading to a more informed and innovative scientific community. By utilizing such tools, researchers can significantly transform their methodologies and enhance their contributions to medicine.

Aurora utilizes principles of quantum mechanics and thermodynamics alongside a sophisticated continuous water model to assess the solvation effects on ligand binding affinities. This methodology is significantly different from the traditional scoring functions typically employed for predicting binding affinities. By integrating entropy and aqueous electrostatic contributions directly into the computations, Aurora's algorithms yield far more precise and reliable binding free energy values. The interaction between a ligand and a protein is fundamentally defined by the binding free energy value. This free energy (F) serves as a thermodynamic measure that correlates directly with the experimentally determined inhibition constant (IC50), influenced by factors such as electrostatic interactions, quantum effects, aqueous solvation forces, and the statistical characteristics of the molecules involved. Non-additivity in F arises primarily from two key components: the electrostatic and solvation energy, and the entropy, which together contribute to the complexity of ligand-protein interactions. Understanding these contributions is essential for the accurate prediction of binding affinities in drug design and molecular biology.

BioNeMo is a cloud service and framework for drug discovery that leverages AI, built on NVIDIA NeMo Megatron, which enables the training and deployment of large-scale biomolecular transformer models. This service features pre-trained large language models (LLMs) and offers comprehensive support for standard file formats related to proteins, DNA, RNA, and chemistry, including data loaders for SMILES molecular structures and FASTA sequences for amino acids and nucleotides. Additionally, users can download the BioNeMo framework for use on their own systems. Among the tools provided are ESM-1 and ProtT5, both transformer-based protein language models that facilitate the generation of learned embeddings for predicting protein structures and properties. Furthermore, the BioNeMo service will include OpenFold, an advanced deep learning model designed for predicting the 3D structures of novel protein sequences, enhancing its utility for researchers in the field. This comprehensive offering positions BioNeMo as a pivotal resource in modern drug discovery efforts.

IPA can also help analyze small-scale experiments that produce gene and chemical lists. IPA allows for targeted searches on genes, chemicals, and drugs. It also allows the creation of interactive models of experimental system. Data analysis and search capabilities allow for the understanding of the significance of data, targets, or candidate biomarkers within larger biological or chemical systems. The Ingenuity Knowledge Base contains highly structured, detail-rich chemical and biological findings that backs the software. Learn more about QIAGEN Ingenuity Pathway Analysis. Comparison Analysis determines which pathways, upstream regulators and diseases are most important. It can also be used to identify biological functions across time, doses, and other conditions.

Rare diseases often lack comprehensive research, resulting in insufficient knowledge about essential elements for an effective drug discovery initiative. Our innovative AI platform, Healnet, addresses these issues by scrutinizing vast amounts of drug and disease data to uncover new connections that may lead to potential treatments. Utilizing cutting-edge technologies throughout the discovery and development process allows us to operate multiple phases simultaneously and on a large scale. The conventional approach of focusing on a single disease, target, and drug is overly simplistic, yet it remains the standard for most pharmaceutical companies. The future of drug discovery is driven by AI, characterized by parallel processes and an absence of rigid hypotheses, fundamentally integrating the three core paradigms of drug discovery into a cohesive strategy. This new paradigm not only enhances efficiency but also fosters creativity in developing solutions for complex health challenges.

BIOVIA solutions foster an unparalleled environment for scientific management, enabling organizations focused on science to develop and interlink innovations in biology, chemistry, and materials to enhance our quality of life. The leading BIOVIA portfolio emphasizes the seamless integration of diverse scientific disciplines, experimental workflows, and information needs throughout the entire spectrum of research, development, quality assurance, quality control, and manufacturing. It boasts capabilities spanning Scientific Informatics, Molecular Modeling and Simulation, Data Science, Laboratory Informatics, Formulation Design, BioPharma Quality and Compliance, as well as Manufacturing Analytics. BIOVIA is dedicated to accelerating innovation, boosting productivity, enhancing quality and compliance, lowering costs, and expediting product development for clients across various sectors. Additionally, it plays a crucial role in managing and unifying scientific innovation processes and information throughout the entire product lifecycle, ensuring a comprehensive approach to scientific advancement.

Amazon Neptune is an efficient and dependable graph database service that is fully managed, facilitating the development and operation of applications that handle intricate, interconnected datasets. At its heart, Amazon Neptune features a specialized, high-performance database engine tailored for the storage of billions of relationships while enabling rapid querying with latency measured in milliseconds. It accommodates widely-used graph models, including Property Graph and W3C's RDF, along with their associated query languages, Apache TinkerPop Gremlin and SPARQL, which simplifies the process of crafting queries for navigating complex datasets. This service supports various graph-based applications, including recommendation systems, fraud detection mechanisms, knowledge graphs, drug discovery initiatives, and enhanced network security protocols. With a proactive approach, it enables the detection and analysis of IT infrastructure threats through a multi-layered security framework. Furthermore, it allows users to visualize their entire infrastructure to effectively plan, forecast, and address potential risks, while also enabling the creation of graph queries for the near-real-time identification of fraudulent patterns in financial and purchasing activities, thereby enhancing overall security and efficiency.

ChemOffice significantly boosts the efficiency of scientists by providing tools that allow them to systematically organize and investigate their compounds, reactions, and relevant properties, transforming raw data into valuable insights that facilitate more informed decision-making. Additionally, ChemDraw for Excel integrates chemical data into Excel, empowering chemists to leverage Excel's analytical capabilities to sort, organize, and enrich their compound datasets while investigating structure-activity relationships. Meanwhile, Chem3D allows chemists to create three-dimensional models of their compounds, enabling them to examine the spatial arrangement and properties of these molecules to optimize their efficacy or selectivity. Furthermore, ChemFinder functions as an intelligent personal database, assisting scientists in managing their compound collections and enabling them to search and correlate chemical structures with their properties effectively. This comprehensive suite of tools collectively streamlines the workflow for researchers, enhancing their ability to conduct high-quality scientific work.

BIOVIA COSMO-RS serves as an extensive toolkit for modeling and forecasting fluid phase characteristics, which empowers professionals such as chemical engineers, chemists, formulation experts, and materials scientists to innovate and develop solutions more rapidly and effectively than relying solely on traditional testing and experimentation methods, thereby enhancing innovation while minimizing costs. The simulations conducted using COSMO-RS are grounded in a solid scientific framework, guaranteeing dependable predictions across the entire spectrum of liquid-state chemistry. By employing a first-principle methodology, it is capable of forecasting the properties of new compounds that have yet to be synthesized, pushing the boundaries of known chemical territory. The team behind BIOVIA consists of the original creators of COSMO-RS, providing prompt assistance and unparalleled expertise to tackle even the most complex challenges in solution thermodynamics. Additionally, the primary advantages encompass a solid scientific basis that merges quantum chemistry with thermodynamics to assure both accuracy and reliability, fostering a deeper understanding of fluid behaviors in various contexts. This comprehensive approach not only streamlines the research process but also opens up new avenues for exploration within the field.

We have established ourselves as frontrunners in the fields of ADMET property prediction, physiologically-based pharmacokinetics (PBPK) modeling, pharmacometrics, and quantitative systems pharmacology/toxicology, a status achieved through the achievements our clients have experienced while partnering with us. Leveraging over two decades of expertise, our skilled team excels at transforming complex scientific concepts into accessible software solutions, while also offering specialized consulting services that bolster drug discovery, clinical development research, and regulatory submission processes. Our dedication to client success drives our continuous improvement and innovation in these critical areas.

Since its inception in 1985, ChemDraw® solutions have delivered exceptional features and integrations that allow users to swiftly convert their concepts and sketches into impressive publications. ChemOffice+ Cloud serves as a comprehensive suite for chemistry communication, transforming chemical illustrations into valuable knowledge by streamlining the management, reporting, and presentation of chemistry research. This powerful suite is specifically designed to enhance and expedite communication within the field of chemistry. Building upon the foundation of ChemDraw Professional, ChemOffice+ Cloud offers a wide range of advanced tools that support scientific inquiry and collaboration. The once tedious process of drafting reports for chemical research is now significantly more efficient thanks to ChemOffice+ Cloud. With its robust capabilities for searching, reusing, selecting, and organizing chemical structures and data, chemists can effortlessly create polished PowerPoint presentations and manuscripts, making their work more accessible and impactful. This transformation not only saves time but also elevates the overall quality of research dissemination in the scientific community.

Business intelligence in the global biopharmaceutical sector focuses on drug patent dynamics and the entry of generics. It is essential to forecast future budget needs and proactively seek out generic alternatives. Analyzing the achievements of past patent challengers provides insights into the competitive landscape and informs research directions. This analysis plays a crucial role in guiding portfolio management strategies for upcoming drug development projects. Additionally, anticipating the expiration of patents on branded drugs, pinpointing potential generic suppliers, and managing branded drug inventory effectively are vital. Furthermore, acquiring detailed formulation and manufacturing data helps in identifying key formulators, repackagers, and relabelers to streamline operations and enhance market positioning. Understanding these elements can significantly bolster strategic decision-making in the biopharmaceutical industry.

Predict absorption distribution metabolism and excretion properties from chemical structure. This collection of high-quality calculations for pharmacokinetic properties can be used to support high-throughput screening of libraries. It also provides insight into pharmacological effects and can help ensure that products are safe for humans.

alvaModel is an advanced software application designed for the construction, validation, comparison, and implementation of QSAR and QSPR models. It excels in supporting both regression and classification tasks through the use of molecular descriptors and fingerprints, emphasizing transparency, interpretability, and scientific rigor in its models. This software offers a variety of data splitting techniques, variable selection approaches, and modeling algorithms, as well as thorough internal and external validation methods. Additionally, alvaModel includes diagnostic visualizations, applicability domain evaluations, and tools for model comparison, which aid users in pinpointing reliable and predictive modeling solutions. Crafted in accordance with the highest standards of chemometrics, alvaModel promotes the creation of interpretable models that align with OECD guidelines for QSAR validation, making it ideal for both research and regulatory uses. Its user-friendly graphical interface walks users through the entire modeling process while providing comprehensive control over every aspect of the modeling journey, ensuring a seamless experience. Ultimately, alvaModel stands out as a valuable asset for chemists and researchers aiming to enhance their modeling capabilities.

Cytel stands out as a prominent global innovator in software for clinical trial design, biometrics, and advanced analytics, focusing on maximizing the efficiency of clinical trials while aiding pharmaceutical companies in harnessing the full scope of both clinical and real-world data. Established in 1987 by renowned statisticians Cyrus Mehta and Nitin Patel, Cytel has consistently been a leader in adaptive clinical trial technology and the field of biostatistics. Its software solutions, notably the East Horizon platform, facilitate accurate trial design and simulation, employing adaptive and Bayesian methodologies to enhance protocols and expedite the drug development process. The East Horizon platform serves as a comprehensive integration of Cytel's reliable software offerings, featuring R integration that significantly improves trial design functions. Furthermore, Cytel provides the Xact software suite, which is an all-encompassing toolkit designed for statistical analysis of small datasets, including those with sparse and missing data. By continuously innovating and expanding its product offerings, Cytel remains committed to providing cutting-edge solutions that meet the evolving needs of clinical research.

We are a biotechnology firm in the clinical stage, dedicated to unraveling biological complexities through the integration of cutting-edge innovations spanning biology, chemistry, automation, machine learning, and engineering, all aimed at revolutionizing drug discovery. Our approach allows for enhanced precision in biological manipulation with advanced techniques like CRISPR genome editing and synthetic biology. We also achieve reliable automation for intricate laboratory processes at an unprecedented scale through the use of sophisticated robotics. By employing neural network architectures, we conduct iterative analyses and draw insights from extensive, intricate datasets generated in-house. Furthermore, we are boosting the adaptability of high-performance computing capabilities through cloud-based solutions. Our initiative harnesses new technologies to foster continuous learning cycles around our datasets, establishing us as a next-generation biopharmaceutical enterprise. This is achieved through a harmonious integration of hardware, software, and data, all dedicated to the industrialization of drug discovery. We are transforming the conventional drug discovery pipeline and boast one of the most extensive, diverse, and in-depth pipelines among technology-driven drug discovery companies. Ultimately, our mission is to enhance the efficiency and effectiveness of drug development, paving the way for breakthrough therapies.

The Kanteron Platform assimilated a wide array of medical images, digital pathology slides, genomic sequences, and patient information from various modalities, scanners, sequencers, and databases, delivering a comprehensive data toolkit to all teams within hospital networks. It emphasizes pharmacogenomics to avert adverse medication events and facilitates the application of precision medicine at the point of care by integrating data sources on drug-gene interactions that were formerly only accessible in less user-friendly formats, such as tables found in PDF documents. By incorporating major pharmacogenomic databases like PharmGKB, CGI, DGIdb, and OpenTargets, it enables users to customize their queries according to specific gene families, types of interactions, and drug classifications. Additionally, its adaptable AI allows users to select the dataset that best aligns with their specific use case, applying it effectively to pertinent medical images. This robust functionality not only enhances the accuracy of medical insights but also fosters a more personalized approach to patient care.

Discngine Assay serves as a comprehensive laboratory informatics platform that unifies all stages of plate-based assays into a streamlined, compliant, and effective workflow, proving to be a vital resource for screening research laboratories. This platform empowers researchers to optimize their entire High Throughput Screening process, encompassing everything from managing samples and analyzing assay data to data storage and qualifying liquid handling instruments. With its user-friendly interface and powerful API, Discngine Assay integrates effortlessly with laboratory equipment and the existing IT infrastructure, facilitating effective data collection and processing. Tailored to expedite the discovery of new molecules, it meets the requirements of the pharmaceutical, biotech, and contract research organization sectors, thereby promoting collaboration and fostering innovation within life sciences research. Furthermore, its ability to adapt to various laboratory environments makes it a versatile solution for evolving research demands.

InSilicoTrials.com is an online platform that offers a user-friendly environment for computational modeling and simulation, featuring a range of integrated, easy-to-navigate in silico tools. It primarily serves professionals in the medical device and pharmaceutical industries. The in silico tools designed for medical devices facilitate computational testing across various biomedical fields, including radiology, orthopedics, and cardiovascular health, during the stages of product design, development, and validation. For the pharmaceutical industry, the platform grants access to in silico tools that support all phases of drug discovery and development across diverse therapeutic areas. We have developed a unique cloud-based platform grounded in crowdscience principles, allowing users to efficiently utilize validated models and reduce their R&D expenses. Additionally, users can explore a continuously expanding catalog of models available for use on a pay-per-use basis, ensuring flexibility and accessibility for their research needs.