Alternatives to Bruker Drug Discovery

SYNTHIA™ Retrosynthesis software, developed by computer scientists and coded by chemists, allows scientists to quickly and easily navigate novel and innovative pathways for novel and previously published target molecules. You can quickly and efficiently scan hundreds pathways to identify the best options for your needs. Discover the most cost-effective route to your target molecule with the latest visualization and filtering features. You can easily customize the search parameters to eliminate or highlight reactions, reagents, or classes of molecules. Explore innovative and unique syntheses to build your desired molecule. Easy to generate a list for starting materials that are commercially available for your synthesis. ISO/IEC 27001 Information Security Certification will guarantee the confidentiality, integrity and protection of your data.

The biopharmaceutical market is complex today. There are growing demands for better specificity and safety, new treatment classes, and more complex mechanisms of disease. To keep up with this complexity, we need to have a better understanding of therapeutic behavior. Simulation and modeling provide unique opportunities to explore biological and physical processes down to the atomic levels. This can be used to guide physical experimentation and accelerate the discovery and development process. BIOVIA Discovery Studio brings together more than 30 years of peer reviewed research and world-class in-silico techniques like molecular mechanics and free energy calculations. It also allows for biotherapeutics developmentability and other related topics into one environment. It gives researchers a complete toolkit to explore the nuances in protein chemistry and to catalyze the discovery of small and big molecule therapeutics, from Target ID to Lead Optimizement.

ClinCapture's mission is to create software that saves lives. ClinCapture's technology reduces the cost of clinical trials. It streamlines data capture and protects patient privacy. Clincapture is a platform that facilitates the evaluation and development drugs, biologics and devices that have the potential to treat a wide variety of medical conditions or diseases.

BIOiSIMTM, a revolutionary 'virtual drug engine', is the first-in-class tool that allows drug developers to narrow down the number drugs that have potential value in treating or curing specific diseases or illnesses. We offer a variety of translational-based solutions that can be customized for your clinical and pre-clinical programs. All of these solutions are based on our BIOiSIMTM platform, which is a proven and validated platform for small molecules, large molecule, and viruses. Our models are built using data from thousands upon thousands of compounds across seven species, which gives them a robustness that is rare in the industry. The platform is focused on human outcomes and has at its core a translatability tool that transforms insights across species. The BIOiSIMTM platform is available before preclinical animal trials begin, which allows for earlier insights and saves on expensive outsourced experimentation.

Genedata Biologics®, streamlines the discovery of biotherapeutics. This includes bispecifics and ADCs as well as TCRs, CARs-Ts, CARs-Ts, CARs, and AAVs. It integrates all discovery workflows, making it the most popular platform in the industry. This allows you to focus on innovation and is the most widely used. A first-in-class platform that digitalizes biotherapeutic discovery accelerates research. The platform simplifies complex R&D processes by allowing for the creation, tracking, testing, and evaluation of novel biotherapeutics drugs. It can work with any format: antibodies, bi- and multi-specifics as well as ADCs, novel scaffolds, therapeutic proteins, and engineered therapeutic cell line such CAR-T cells and TCRs. Genedata Biologics acts as a central data backbone that integrates all R&D processes. This includes library design, immunizations, selections, panning, molecular Biology, screening, protein engineering and expression, as well as candidate development and manufacturability assessment.

AutoDock is a set of automated docking tools. It predicts how small molecules, such sub- or drug candidates, will bind to a receptor with a known 3D structure. It has been improved over the years to add new functionalities and multiple engines were developed. AutoDock 4 is the current version, and AutoDock Vina is the latest. AutoDock-GPU is an accelerated version that runs hundreds of times faster than the original single CPU docking code. AutoDock 4 is actually composed of two main programs. Autodock docks the ligand to a list of grids that describe the target protein. Autogrid pre-calculates the grids. The atomic affinity grids can also be visualized. This can be used to help organic synthetic chemists create better binders.

Site-Identification by Ligand Competitive Saturation generates 3D maps (FragMaps), showing interaction patterns for chemical functional group with your target molecule. Site-Identification through Ligand Competitive Saturation generates 3D maps, (FragMaps), of interaction patterns for chemical functions groups with your target molecule. SILCS reveals the intricacies and provides tools to optimize ligand scaffolds through qualitative and quantitative binding pocket insights. This allows for faster and more effective drug design. SILCS employs multiple small molecule probes that have different functional groups and explicit solvent modeling to perform protein target mapping. Visualize positive interactions with the target macromolecule. Get insights to design better binding agents with the best functional groups.

Advanced molecular modeling can transform drug discovery and materials research. Our physics-based computational platform combines differentiated solutions for predictive modelling, data analytics, collaboration, and collaboration to allow rapid exploration of chemical space. Our platform is used by industry leaders around the world for drug discovery and materials science in fields such as aerospace, energy, semiconductors and electronics displays. The platform powers our drug discovery efforts, from target identification through hit discovery to lead optimization. It also powers our research collaborations to create novel medicines for critical public healthcare needs. Our team includes more than 150 Ph.D. scientists. This allows us to invest heavily in R&D.

Artificial Intelligence and RNA sequencing (RNA–seq data) are both a necessity and a way to develop therapeutics that target splicing mistakes. Machine learning allows us to quickly identify new splicing mistakes and design therapeutic compounds to correct them. SpliceCore, our AI platform for RNA therapeutics research, is what we call SpliceCore. This technology platform was specifically designed for the analysis and interpretation of RNA sequencing data. It can identify, validate and test hypothetical drug targets quicker than traditional methods. Our proprietary database of over 5 million potential RNA-splicing errors is the heart of SpliceCore. It is the world's largest database of splicing mistakes and is used to test all RNA sequencing data that is submitted for analysis. Scalable cloud computing allows us to process large amounts of RNA sequencing data efficiently at a higher speed and lower cost, thereby exponentially accelerating therapeutic innovation.

Our AI engine transforms drug discovery. Our discoveries help create better medicines faster. Our AI-enabled discovery portfolio is co-owned and owned by prominent investors. Atomwise developed a machine-learning-based discovery engine that combines the power of convolutional neural networks with massive chemical libraries to discover new small-molecule medicines. People are the key to redefining drug discovery using AI. We are committed to creating the best AI platform possible and using it to transform small-molecule drug discovery. To help drug developers achieve their goals, we have to tackle the most difficult and seemingly impossible targets. We also need to streamline the drug discovery process in order to make it more efficient. The ability to screen trillions of compounds in silica increases the chance of success. Demonstrates exceptional model accuracy, surpassing the challenge of false negatives.

Eidogen-Sertanty’s Target Informatics platform (TIP), is the first global structural informatics system. It enables researchers to examine the druggable genome from an structural perspective. TIP increases the rapidly expanding body experimental protein structure information and transforms structure based drug discovery from an inefficient, data-scarce discipline to a high-throughput science with rich data. TIP is a tool that bridges the knowledge gap between bioinformatics (bioinformatics) and cheminformatics. It provides drug discovery researchers with a knowledge bank of information that is both unique and highly complementary to existing bio- and cheminformatics platform information. TIP's seamless integration between structural data management technology and unique target-to-lead analysis capabilities enhances every stage of the discovery pipeline.

Our immunotherapy approach starts with our understanding of antigens. We also know which neoantigens will be present on cells by Human leukocyte antigen molecules. This will allow us to identify the ones that will be transcribed and translated. Gritstone EDGETM is our machine-learning-based platform that enables us to accomplish this. It is difficult to develop cancer immunotherapies that include tumor specific neoantigens. Tumors can have hundreds of mutations. However, only a small number of these mutations result in true tumor-specific antigens. We used EDGE's new integrated neural network architecture to train it with millions of data points from hundreds tumor and normal tissue samples taken from patients of different ancestries.

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

Many diseases and conditions are linked to the microbiome. Discover how Kaleido is transforming the promise of microbiome into solutions that benefit patients. The human microbiome, which includes bacteria, viruses, archaea, fungi and other organisms, is a collection of over 30 trillion microbes. Research has increased exponentially over the past decade on the effects of the microbiome on human health, including diabetes, heart disease, Parkinson's disease, and cancer. This complex microbial ecosystem has been called a "newly discovered" organ. Many other human organs are worth tens to billions of dollars for therapeutics that modify physiology and treat disease. The microbiome organ is a promising therapeutic option.

InSilicoTrials.com, a web-based platform that allows users to create and simulate computational models and simulations. There are many easy-to-use tools in silico. The platform is primarily for users in the medical device and pharmaceutical industries. In silico tools for medical devices allow computational testing in different biomedical areas such as radiology, orthopedics, and cardiovascular during product development, validation, and design. The platform offers access to in-silico tools for the pharmaceutical industry, which can be used at all stages of drug discovery and development. It also covers a variety of therapeutic areas. The only cloud-platform built on crowdscience makes it easy to access validated models and reduce your R&D expenses. There is a growing list of models that can be used on a pay-per-use basis.

A series of carefully planned experiments can help you discover biological biomarkers, uncover disease mechanisms, find new drugs, or identify protein levels changes. It's easy to unlock the power of MS/Proteomics. This allows you to focus on the biological complexity and get closer to the moment for discovery. Our automated, repeatable workflow makes it easier to start experiments and reduce turnaround times. This gives you the flexibility and control to make decisions and act on them immediately. Our proteomics data processing workflow allows you to concentrate on biological insights and human to human collaboration. It is built to scale repeatedly. We have made repetitive and heavy processing easy to use the cloud, making it a seamless and enjoyable experience. Intelligent Proteomics seamlessly integrates complex moving parts to allow larger experiments to be processed, analyzed, and reported with ease.

Makya is a user-friendly SaaS-based platform for AI-driven drug design focusing on Multi-Parametric Optimization. It allows the design of novel compounds that are easy to make in accordance with a multiobjective blueprint at unprecedented speeds, performance and diversity. Makya has multiple generative algorithms that cover different use cases, from hit discovery to lead optimizing: fine-tuning to find optimal solutions in your chemical space according to your project blueprint; novelty to find new ideas of high novelty for rescaffolding/hit discoveries; forward to design a focused collection of compounds easily accessible using commercial starting materials. The new Makya 3D Module enhances the user-experience and scientific utility of Makya. Makya 3D offers a wide range of 3D modeling capabilities in both ligand and structure-based pipelines. You can now use these 3D scores to guide generation natively within Makya.

LiveDesign is an enterprise information platform that allows teams to collaborate, design, experiment, analyze, track, and report in one central platform. You can capture ideas and model data. Create and store virtual compounds in a central database. Use advanced models to prioritize new designs. Integrate biological data and model results from federated corporate databases. Use sophisticated cheminformatics for faster analysis and development of compounds. Advanced physics-based methods are combined with machine learning techniques to quickly improve prediction accuracy. Remote team members can collaborate in real-time. You can collaborate with remote team members to share ideas, test, revise, or advance chemical series without losing sight of your work.

Global biopharmaceutical drug patents and generic entry business intelligence. Anticipate future budget needs and proactively find generic sources. Examine past successes of patent challengers to identify research paths for competitors. Inform portfolio management decisions on future drug development. Predict brand drug patent expiration, identify generic supplier, and prevent overstocking of branded drugs. Get formulation and manufacturing information. Identify final formulators, repackagers and relabelled.

AIDDISON™, a drug discovery software, combines the power and efficiency of artificial intelligence (AI), computer-aided design (CADD), and machine learning (ML) to provide a valuable toolkit that can be used for medicinal chemistry. It is a unified platform that integrates all aspects of virtual screening, including ligand-based design and structure-based design. It also supports methods for in silico lead optimization and discovery.

Rare diseases are often poorly understood and only a small amount of information is available to support drug discovery programs. Healnet, an AI platform that analyzes millions of data points related to drug and disease, helps overcome these challenges. It identifies novel connections that could lead to new treatment options. We can simultaneously run multiple stages at scale by applying frontier technologies throughout the discovery and development process. It's a simple model that can be used by almost all pharmaceutical companies. One disease, one target and one drug. The next generation in drug discovery is AI-powered and parallel. The key three paradigms of drug discovery are combined.

Aurora uses quantum mechanics, thermodynamics and an advanced continuous-water model for solvation effects in order to calculate ligand binding affinities. This approach is significantly different from the binding affinity predictions that are often based on scoring functions. Aurora algorithms are able to produce more precise and reliable binding free energies values by including the entropy as well as the aqueous electrostatic contribution directly into the calculations. The binding free energy is the measure of interaction between a ligand and a protein. The free energy (F), which is a thermodynamic quantity directly related to the experimentally measurable value for inhibition constant (IC50), and depends on the statistical properties of interacting molecules as well as electrostatic, quantum, and aqueous solver forces, is a thermodynamic quantity. Two major contributors to F's non-additivity are 1) the electrostatic and the solvation energy, and 2) the entropy.

Use AI to accelerate the journey from bench-research and laboratory insights to life-changing therapies. Reduce your reading time to minutes and gain up to 90% more research productivity. With a high-precision and high-accuracy research, you can cut through the noise to navigate the ever-growing amount of scientific literature. Save time, reduce bias, and increase the odds of discovering novel things. Explore disease biology in depth and discover advanced targets. Causaly’s high-precision graph of knowledge consolidates evidence from millions publications, allowing for unbiased, deep scientific exploration. You can navigate cause-and effect relationships in biology without being an expert. Discover hidden connections by viewing all scientific documents. Causaly’s powerful AI machine is able to read millions of published biomedical publications in order to support better research and decision-making.

Dotmatics is the global leader in R&D scientific software that connects science, data, and decision-making. More than 2 million scientists and 10,000 customers trust Dotmatics to accelerate research and help make the world a healthier, cleaner, and safer place to live.

Kanteron Platform ingested digital pathology slides, medical images, and patient data from modalities and sequencers. It then provided a complete data toolkit for all hospital network teams. Precision Medicine at the point-of-care: Pharmacogenomics is used to prevent adverse medication events. It also incorporates data sources that have drug-gene interactions that were not previously available in easily accessible formats (e.g. Tables in a PDF document) and implementing the major Pharmacogenomic Databases (like PharmGKB or DGIdb, OpenTargets ...). This allows the user to refine their query to specific gene families, types, interactions, etc. Flexible AI allows you to choose the data set that is most appropriate for your use case and then apply it to your relevant medical images.

We are a biotechnology company in clinical stage. We decode biology by integrating technological innovations across biology and chemistry to industrialize drug discovery. CRISPR genome editing and synthetic Biology allow for greater control over biology. Advanced robotics allows for reliable automation of complex laboratory research on an unprecedented scale. Neural network architectures allow for iterative analysis and inference from large, complex, in-house data sets. Cloud solutions increase the flexibility of high-performance computation. To build a next-generation biopharmaceutical business, we are using new technology to create virtuous learning cycles around datasets. A synchronized combination hardware, software, and data that is used to industrialize drug discovery. Redefining the traditional drug discovery process. One of the most extensive, broadest, and deepest pipelines in any technology-enabled drug company.

These intricate, complex machines are proteins. They are the building blocks of all biological processes, not only in your body, but in every living being. They are the building blocks of all life. There are currently around 100 million distinct proteins. Many more are discovered every year. Each protein has a unique 3D shape, which determines how it functions and what it does. It is expensive and time-consuming to determine the exact structure of each protein. This means that we only have a small number of proteins in our database. This gap is rapidly growing and we need to be able to predict the structure of millions unknown proteins. This could help us not only tackle disease but also help us find new medicines more quickly. It may also help us unlock the mysteries of life.

VeraChem LLC was established in 2000 to advance computer-aided drug discovery. These methods are implemented in high-performance software and provide comprehensive support to users. This is a key strategy of VeraChem LLC for product development. Current VeraChem software capabilities include host-guest and protein-ligand binding affinity prediction, fast calculation and computation of partial atomic charges for druglike compounds, computation and force of energies and forces using all the most commonly used empirical force field, automatic generation and generation of alternate resonance forms for drug-like substances, conformational search using the powerful Tork algorithm and automatic detection and removal of topological and 3D-molecular symmetries. VeraChem's software packages were built from a modular code base.

DNAnexus Apollo™, accelerates precision drug discovery through collaboration that draws critical insights from omics data. Precision drug discovery requires the collection and analysis of large volumes of clinical and omics data. These data sets are extremely rich, but many legacy and home-grown informatics tools cannot handle their complexity and size. Silos, insufficient collaboration tools, and complex regulatory and security requirements can all hinder precision medicine programs. DNAnexus Apollo™, which supports precision drug discovery programs, empowers scientists and clinicians to analyze and explore omics and clinical data in a single environment built on a robust and scalable cloud platform. Apollo allows them to share data, tools and analyses securely with peers and colleagues from all over the world, regardless of whether they are on another floor or another continent.

Software that will improve safety, traceability and integration of your Pharmacotechnics and Medicines Manufacturing Unit. PharmaSuite is not designed to be used by all pharmacy services. We have broken PharmaSuite into functional modules that allow you to manage different units in the most efficient manner. Management of the Pharmacotechnics and Drug Preparation units (parenteral mixtures). Management of parenteral nutrition.

Scitara DLX™ provides a fast connectivity infrastructure for any instrument used in life science laboratories. It is fully compliant and auditable, and can be accessed from any cloud-based platform. Scitara™, a universal digital data network, connects all instruments, resources, apps, and software within the laboratory. The cloud-based platform, which is fully auditable, connects all data sources in the lab, allowing data to flow freely across multiple endpoints. Scientists can now spend their time on scientific research and not waste it trying to solve data problems. DLX corrects and curates flight data to support the creation of precise, structured data models that feed AI/ML systems. This supports a successful digital transformation strategy for the pharma and biopharma sectors. The ability to access scientific data allows for faster decision-making and drug discovery, which helps bring drugs to market quicker.

Clara's domain specific tools, AI pretrained models, accelerated applications, and accelerated AI applications are enabling AI advances in many fields, including medical device, imaging, drug discovery and genomics. Holoscan allows you to explore the entire pipeline of medical device deployment and development. With the NVIDIA IGX Developer Kits, you can build containerized AI apps using the Holoscan SDK. The NVIDIA IGX SDK includes pre-trained AI model, healthcare-specific acceleration libraries and reference applications for medical devices.

Our reputation as thought leaders in the areas of ADMET property prediction, physiologically-based pharmacokinetics (PBPK) modeling, pharmacometrics, and quantitative systems pharmacology/toxicology is earned through the success our clients have found through their relationship with us. With over 20 years of experience, we have the ability to translate science into software that is easy to use. We also provide expert consulting support for drug discovery, clinical research, and regulatory submissions.

ArgusLab is a program that allows you to molecular model, graphically, and design drugs for Windows operating systems. Although it is becoming a little outdated, it remains very popular. There have been more than 20,000 downloads to date. ArgusLab can be downloaded for free. You don't have to sign anything. If you're teaching a class in which ArgusLab might be useful, you can print as many copies as necessary. You cannot redistribute ArgusLab to other websites or sources. You may link to this website on your own websites, however. ArgusLab is being port to the iPad in a low-key effort. I have also worked with Qt cross-platform development environment to support Mac, Linux, and PC.

BioNeMo, an AI-powered cloud service for drug discovery and framework, is built on NVIDIA NeMo Megatron. It is used to train and deploy large biomolecular Transformer AI models at supercomputing scale. The service provides pre-trained large language models (LLMs), native support for common file types for proteins, DNA, and chemistry, as well as data loaders for SMILES molecular structures and FASTA amino acid and nucleotide sequencings. You can also download the BioNeMo framework to run on your own infrastructure. ESM-1, which is based on Meta AI’s state-of the-art ESM-1b and ProtT5 respectively, are transformer-based protein-language models that can be used for learning embeddings for tasks such as property prediction and protein structure. BioNeMo will offer OpenFold, a deep-learning model for 3D structure prediction and novel protein sequences.

BC platforms uses the latest science, unique technology capabilities and strategic partnerships to accomplish our mission of revolutionizing drug discovery, personalizing care, and transforming medicine. Modular, flexible platform that integrates healthcare data. Open analytics framework seamlessly combines the most innovative methods, technology developments and analytics in one platform. Superior security: ISO 27001 certified and GDPR and HIPAA compliant. A complete product portfolio allows modern healthcare systems to fully embrace personalized medicine. Scalable deployments allow for a robust start and large-scale healthcare operation. Our unique toolbox enables faster translation of research insights into clinical practice. Our unique toolbox helps reduce risk, increase your pipeline value, and advance enterprise data strategy. We remove the barriers to data access and enable rapid insight generation.

Amazon Neptune is a fully managed graph database service that allows you to quickly and reliably build applications that can work with highly connected data sets. Amazon Neptune's core is a purpose-built graph database engine that can store billions of relationships and query the graph with only milliseconds latency. Amazon Neptune supports the popular graph models Property Graph, W3C's RDF, as well as their respective query languages Apache TinkerPop Gremlin, SPARQL. This allows you to quickly build queries that efficiently navigate large datasets. Neptune supports graph use cases like recommendation engines, fraud detection and knowledge graphs. It also powers network security and drug discovery.

adWATCH – AE assists pharmaceutical companies to report adverse events that may occur during clinical trials. adWATCH – AE provides a quick and efficient way for a reporter at a clinic or hospital to generate and manage Adverse Event Reports (AERs), and report to regulatory agencies and government agencies. A negative or dangerous effect is one that a patient experiences due to the use of drugs or medical devices. Adverse event reporting involves the tracking of all medical complaint cases. This allows for the generation of MedWatch reports, CIOMS and additional reports for management. adWATCH – AE allows researchers, physician-investigators, Contract Research Organizations, clinical trial specialists, and other healthcare professionals to create and file AERs in FDA mandated MedWatch or CIOMS format.

Automated surveillance of any source of data, fully integrated with a GVP IX-compliant signal management platform. GVP-IX compliant signal-management platform integrated into Evidex and ready-to-use off-the-shelf. Modernize and audit-proof all your management processes without the need to switch between platforms or services. Your safety data is worth more than you think. Automating signal detection and management allows you to focus on your organization's value, not just regulatory requirements. Safety signals can be identified from traditional sources such as ICSR databases, FDA Adverse Events Reporting System (FAERS), VigiBase, and clinical trial data. Incorporate new data sources like claims, EHR, or other unstructured data. These data sources can be combined seamlessly to improve signaling algorithms, increase validations and assessment efficiency, and provide faster answers for drug safety questions.

Preclinical information is found in many laboratories. It can be stored in multiple systems within the laboratory and with several external partners. Team members are unable to have clear and informed decisions without a unified solution because they lack transparency in core business data. Pristima, a fully integrated digital laboratory execution platform, features intelligent workflows, task automation, and data and information management throughout the entire preclinical process. Xybion's preclinical platform provides a central data repository as well as a standard archive platform. This platform can help you increase productivity and lower costs. With complete transparency across all platforms, gain visibility into the information that is there and take actions based on your current business needs. Effective data management can reduce the time it takes to submit final SENDs from end-of-study.

Cortellis™, a suite of life science intelligence software solutions, reveals hidden insights in data. This allows you to make better informed decisions throughout the R&D process. We have taken out the tedious work of finding, integrating and analysing data so that you can concentrate on the crucial decisions required to bring your products to market quicker. Cortellis provides unique data analysis that is rich in domain knowledge, industry insight, and therapeutic expertise. This allows you to unlock hidden insights that will allow you to make data-driven decisions that drive innovation. With the most comprehensive and deepest intelligence, you can get precise and actionable answers to specific questions throughout the R&D process. Cortellis is an indispensable part your daily work flow.

Genedata Imagence®, allows you to train a deep neural net to classify cellular characteristics in HCS images. This will give you objective, high-quality results. It automates your analysis to allow assay biologists to harness the power of deep learning algorithms. Genedata Imagence allows biologists to immediately and directly analyze HCS imaging data with sophisticated deep learning techniques using no specialized algorithmic knowledge. Don't hide your analysis behind abstract lines of code. Genedata Imagence's intuitive interface makes it easy to QC and explore data at every stage.

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.

Metabolon offers the largest Level 1 library available in the metabolomics field. Over 5,400 entries are contained in our proprietary library, which has been built over 20 years. The majority of entries are Level 1, accounting for approximately 85% of our library (4,600 entries). However, some are level 2 (approximately 15 % of the entries), due to a lack commercial standards to qualify for Level 1. Metabolon's unmatched library breadth, industry-leading annotation confidence level and unmatched library depth enable us to provide our clients with accurate, highly actionable insight for their scientific or clinical inquiries. Metabolomics is applicable to a wide variety of research, including soil health, food nutrition, preclinical research and clinical trials. Whether you are looking for trends within a group, or modifying an individual's treatment plan, metabolomics will help you find the answers you need.

You can share clinical and pre-clinical knowledge easily across your organization using a single platform that integrates all the tools you need. Phoenix WinNonlin is the preferred choice for non-compartmental analysis, toxicokinetic modeling, pharmacokinetic, and pharmacokinetic (PK/PD), modeling by more than 6,000 researchers from biopharmaceutical companies and academic institutions. It also includes 11 global regulatory agencies including the US FDA (EMA), PMDA, and more. The Phoenix Platform features population PK/PD modeling using Phoenix NLME and Level 1 correlation via the Phoenix IIVC Toolkit. Validation Suites allow for quick and easy software validation in less than 30 minutes.

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.

Polly allows you to harness the power of biomedical information. The Polly Platform allows you to scale batch jobs, workflows and visualization applications. Polly supports resource pooling, optimizes resource allocation based upon your usage requirements, and makes use of spot instances when possible. This results in optimization, efficiency, quicker response time, and lower costs for resources. Access a dashboard that allows you to monitor and track resource usage and costs in real-time. This will allow you to reduce overheads when resource management is done by your IT team. Polly's infrastructure is built around version control. Polly uses a combination dockers and interactive notebooks to ensure version control for your analyses and workflows. We have created a mechanism that allows data, code, and the environment to co-exist. This, along with cloud data storage and the ability for users to share projects, ensures reproducibility in every analysis.

We combine clinical and experimental data with machine learning to improve precision medicine and navigate human disease biology. Contingent AI™, a patent-pending technology, understands data relationships to provide sophisticated insights. We tackle data bias by iterating upon machine learning models that are based on decisions made during the feature engineering and pre-processing stages. We use zebrafish, cell, and other phenotypic animals models to validate in-silico predictions in in vivo experiments. We also genetically modify them in vitro to improve translation. We quickly incorporate new data into machine learning models by using active learning and computer vision on validated models of cardiac, central nervous system, and rare disorders. We quickly incorporate new data into machine learning models by using active learning and computer vision with validated models of cardiac, central nervous system, and rare disorders.

To reduce attrition rates for molecular entities that are unlikely succeed in nomination as drug candidates, direct new compound synthesizers, and focus animal testing requirements, calculate drug toxicity and safety.

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.