Top 50 Ai For Computational Biology

GenBio AI develops the AI-Driven Digital Organism (AIDO), a unified system of multiscale foundation models for predicting, simulating, and programming biology. AIDO integrates diverse biological data from DNA to single-cell information, enabling holistic understanding and accelerating breakthroughs in drug discovery and bio-engineering.

The Biological Intelligence Company develops scalable AI systems that utilize biological data processing techniques to enhance machine learning models. This technology addresses the inefficiencies in traditional AI training methods by improving data accuracy and reducing resource consumption.

Bioptimus is developing a universal AI foundation model specifically for biological research, enabling researchers to leverage large-scale data analysis and predictive modeling. This technology addresses the challenge of slow and fragmented discovery processes in biomedicine by providing a cohesive platform for accelerating insights and innovations.

The startup develops a drug discovery platform that combines artificial intelligence and synthetic biology to digitize natural diversity and identify potential drug candidates based on their functions. This approach aims to make the discovery of new therapeutics for cancer, metabolic diseases, and infectious diseases more predictable and cost-effective.

This biotechnology startup utilizes a proprietary bio platform that integrates evolutionary biology, human molecular data, and artificial intelligence to identify drug-like molecules. The company focuses on developing small-molecule drug candidates to address serious unmet medical needs in health and disease.

This biotechnology company utilizes artificial intelligence to cultivate and evolve proteins essential for bio-products, enhancing the efficiency of medical research. By providing reliable and rapid access to these proteins, the company addresses the need for improved medical services in the healthcare sector.

3BIGS utilizes bio big data and AI-driven platforms to analyze next-generation sequencing (NGS) data, identifying patterns, mutations, and biomarkers for drug discovery and healthcare insights. The company addresses the challenge of extracting actionable information from vast biological datasets, accelerating the development of targeted therapies and improving patient outcomes.

Salt AI offers a development engine that accelerates AI adoption in life sciences by providing a platform for reproducible AI workflows. It enables faster time-to-output and reduced compute costs through optimized model hosting and visual workflow design, facilitating collaboration for drug discovery and biological research.

The startup offers an AI-enabled life sciences research and development platform that manages controlled-access multimodal data at an enterprise scale. This platform enhances data analysis and automates workflows, enabling researchers to conduct high-impact studies and collaborations with reduced resource requirements.

The startup has developed an artificial intelligence platform for biomarker discovery and sequencing analysis, focusing on extracting biosignatures from single-cell and multi-omics data. This platform enables physicians to efficiently analyze complex disease patterns, reducing data processing time and costs while enhancing insights for patient stratification.

The startup offers a cloud-based data analytics platform that processes and visualizes large omics datasets, including genomics, transcriptomics, and proteomics, to elucidate the molecular mechanisms underlying cellular phenotypes. This technology enhances decision-making in drug research and development, enabling scientists and clinicians to efficiently identify potential treatments for diseases.

Matterworks has developed a Large Spectral Model (LSM) that utilizes AI to analyze unstructured biological data, revealing insights that are typically overlooked. This technology addresses the challenge of interpreting vast amounts of omic data, enabling more effective utilization of biological measurements in biotechnology.

The startup is developing a discovery platform that utilizes advanced computational biology and machine learning to enhance the drug development process for complex age-related diseases. This platform addresses the inefficiencies and high costs associated with traditional drug discovery methods, aiming to accelerate the identification of viable therapeutic candidates.

BioAI Health utilizes multimodal AI and in-silico phenotype projection to identify genetic mutations and drug targets in cancer, enhancing precision medicine. The company addresses the challenge of inefficient disease diagnosis and patient therapy matching by developing digital biomarkers and optimizing clinical research and drug development processes.

The startup develops AI technology that accelerates molecular discovery by utilizing machine learning algorithms to analyze molecular activity for precise identification of potential drug compounds. This approach enhances the efficiency of drug compound discovery and simplifies virtual screening benchmarks for pharmaceutical companies.

Bionl is a no-code biomedical research platform that automates bioinformatics workflows, enabling researchers to configure and execute complex data analyses without programming expertise. The platform addresses the inefficiencies of traditional research methods by providing customizable pipelines, AI-driven data analysis, and access to public datasets, streamlining the research process for scientists and biotech companies.

Deep Origin provides a biotechnology platform that integrates data management, AI-driven analysis, and molecular simulation tools to enhance research and development in life sciences. The platform enables scientists to efficiently manage data, perform complex analyses, and accelerate lead compound discovery, addressing the challenges of data complexity and slow drug discovery processes.

The startup employs an AI-first methodology to fundamentally transform the drug discovery process, utilizing machine learning algorithms to predict molecular interactions and optimize compound selection. This approach addresses the inefficiencies and high costs associated with traditional drug development, significantly accelerating the timeline from research to market.

MNDL Bio utilizes AI-driven computational models to optimize gene expression for recombinant protein production, significantly enhancing yield and reducing costs. The platform replaces traditional trial-and-error methods with precise, data-driven strain engineering, enabling faster time-to-market and improved sustainability in biotech and foodtech applications.

Inceptive utilizes large-scale deep learning to design RNA molecules that perform specific functions within biological systems. This approach enables the development of novel synthetic molecules for creating accessible medicines and biotechnologies that were previously unattainable.

Xaira Therapeutics develops an integrated biotechnology platform that utilizes artificial intelligence and biological data generation to enhance drug discovery and development processes. The company addresses inefficiencies in traditional drug development by providing clients with data-driven insights and predictive models to accelerate the creation of effective therapies.

Tamarind Bio provides a web-based platform that utilizes AlphaFold and other machine learning tools for protein design, antibody engineering, and enzyme optimization. The platform enables researchers to predict protein structures and optimize sequences at scale, significantly reducing the time and resources required for computational biology tasks.

The startup develops analytics software that integrates multi-omic data with AI analytics to enhance drug candidate identification and biomarker discovery. This technology enables precision medicine organizations to streamline research processes and significantly reduce time-to-market for new treatments.

This startup provides an AI-powered platform with advanced machine learning tools and algorithms to accelerate biomolecular design. It enables biotech companies to innovate faster and develop novel therapeutics by streamlining the design process.

Provides an AI-powered platform for analyzing complex medical data, including transcriptomics, epigenomics, and proteomics, using modular machine learning techniques. This system accelerates research by delivering fast, high-quality insights for identifying therapeutic targets, biomarkers, and disease mechanisms, reducing time and cost compared to traditional methods.

This startup provides AI-powered software that accelerates biomedical discovery by automating data generation, analysis, and hypothesis testing. Their platform helps biotech and pharmaceutical scientists quickly derive insights from genomic data, significantly reducing the time and cost associated with traditional research methods.

AccutarBio utilizes artificial intelligence to enhance computational drug design and validate findings through wet lab experiments, significantly reducing the time and cost associated with traditional drug discovery. The company addresses the inefficiencies in the drug development process by providing a data-driven approach that accelerates the identification of viable therapeutic candidates.

Develops machine learning algorithms tailored for pharmaceutical research, focusing on drug discovery and development. These algorithms analyze complex biological data to identify potential therapeutic targets and optimize compound selection, reducing time and costs in bringing new drugs to market.

Prism AI provides an AI-driven multi-omic data management and analytics platform that enables biotech companies and health systems to harness their data for drug development and biomarker discovery. This technology addresses the challenge of inefficient data utilization in creating effective therapies for complex diseases.

The startup utilizes polymathic AI to deliver precision medicine by analyzing complex biological data and patient information. This approach enhances treatment personalization, improving patient outcomes in healthcare by tailoring therapies to individual genetic profiles.

ENPICOM provides a unified bioinformatics platform that integrates scalable data management, machine learning frameworks, and user-friendly analysis tools for lab and data scientists. This solution streamlines biologics discovery and engineering processes, enabling rapid data retrieval and model deployment while reducing reliance on specialized data science support.

OneThree Biotech utilizes biology-driven artificial intelligence to enhance the drug discovery process by predicting molecular interactions and optimizing lead compounds. This approach reduces the time and cost associated with traditional drug development methods, enabling more efficient identification of viable therapeutic candidates.

Imagine Biotech uses AI-driven computational modeling to de-risk drug discovery and development. Their platform provides predictive simulations for ADMET properties and molecular behavior, helping biotech companies and investors identify potential failures early and make more informed decisions.

This microbiome-based life sciences company develops healthcare solutions using big data analytics, mathematical modeling, deep learning, and AI. Their technology analyzes the human microbiome to provide insights that support individual health and well-being.

Pythia Labs develops computational tools that utilize machine learning and molecular modeling to accelerate the discovery and design of biomolecular therapeutics. Their technology addresses the lengthy and costly process of drug development by enabling more efficient identification of viable therapeutic candidates.

ScienceMachine develops AI systems that automate the analysis of complex biological data, enabling scientists to quickly derive insights from lab-collected datasets like RNA-seq. This technology accelerates drug development for biotech and pharmaceutical companies by streamlining data processing and ensuring no valuable scientific information is overlooked.

The startup is developing a proprietary training set that annotates interactions between billions of molecules and entire proteomes, utilizing machine learning models to facilitate rapid discovery in biomedical research. By decentralizing access to this extensive data, researchers can initiate discovery programs more efficiently, significantly accelerating the pace of scientific innovation.

Biomod AI provides an AI-powered platform for biotech researchers to generate DNA sequences and design custom oligos using natural language prompts. This automates complex molecular design tasks, accelerating experimental iteration and discovery in genomics and synthetic biology.

Ellumigen offers an AI-powered platform for analyzing complex multi-omics data from preclinical and clinical studies. It transforms raw sequencing and curated study data into actionable insights through natural language querying and a bioinformatics workbench, accelerating drug discovery and biomarker identification.

BathGenBio utilizes artificial intelligence to analyze extensive biobank data, including genomic and clinical information, to identify novel biomarkers and optimize drug discovery processes. The platform addresses the challenges of precision medicine by enhancing target discovery, drug response prediction, and drug repurposing for medical institutions and biotechnology companies.

BioLM provides a high-throughput, low-cost infrastructure for protein and DNA language modeling, enabling biotech and life science companies to efficiently generate and optimize enzyme and antibody candidates. By leveraging advanced computational methods, BioLM accelerates the lead generation process, allowing clients to screen millions of variants in silico and validate them through wet lab testing.

Deep Space Biology has developed Yotta, an AI-driven platform that processes multi-omic data from living entities, significantly reducing research time from years to minutes. By leveraging 25 years of microgravity research, Yotta enhances drug discovery and enables researchers to formulate more informed hypotheses for complex diseases affecting human health.

The startup develops a bioinformatics platform that utilizes DNA sequence manipulation techniques, including alignment, variant calling, and functional annotation, to identify genes associated with specific functions. This enables biotechnology and pharmaceutical companies to conduct mutational analysis for verifying genetic modifications, thereby enhancing product design and accelerating research timelines.

NeoX Biotech utilizes artificial intelligence and computational biophysics to develop macromolecular and multispecific drugs, focusing on immuno-oncology through the characterization of protein-protein interactions. The company collaborates with global biomedical firms to enhance the efficiency of early-stage drug discovery and development pipelines.

This startup designs novel biologics, aptamers, and small drug molecules using bioinformatics, computational biology, and AI. Their platform enables the development of personalized and targeted treatments, aiming to improve patient outcomes.

This startup provides an AI-powered drug discovery platform that integrates DNA-encoded libraries and computational methods to accelerate therapeutic development. It enables biopharma companies to explore novel biology by offering data generation, sharing, and analysis for rapid validation of predictive technologies.

Bioada utilizes AI-driven biomarker and drug discovery techniques to enhance early disease detection and develop personalized treatment plans. By leveraging advanced algorithms, the company accelerates the identification of novel biomarkers and optimizes drug formulations, improving patient outcomes in precision medicine.

AIBIOS leverages bioinformatics to analyze complex biological data, accelerating the identification of therapeutic candidates for immune diseases. The company addresses the inefficiencies and high costs associated with traditional drug development processes, particularly in the fields of immunology and oncology.

Output is developing Biologically-Aware Generative AI that constructs Large Biological Models to analyze complex biological systems. This technology enables the generation of novel medicines by effectively processing high-dimensional biological data, enhancing disease diagnosis, treatment, and prevention.

The startup develops AI-based software that facilitates the design of novel organic molecules and scaffolds through in silico reaction planning and evaluation. This technology enables medical research centers to efficiently create new drugs, improving the quality and success rate of drug development.