Wellness & Lifestyle

SandboxAQ is a B2B company that utilizes the compound effects of AI and advanced computing to tackle significant societal challenges. Their Large Quantitative Models (LQMs) are applied across diverse fields, including AI drug discovery, new chemicals and materials innovation, cybersecurity, navigation, and medical diagnostics. The platform offers technologies such as AI simulation, cryptography management for enhanced cybersecurity, and AI sensing for global organizations. SandboxAQ's approach focuses on quantitative AI, grounded in physics and chemistry, to provide real-world solutions with clear outputs and reduced uncertainty, making it scalable and proven for high-stakes domains.

WhiteLab Genomics is an AI-driven platform founded in 2019, backed by Y-Combinator, dedicated to accelerating the development of life-saving genomic medicines. The platform utilizes its proprietary AI-led framework, ALFRED (AI-Led Framework for Rational Exploration in Drug Design), which combines advanced algorithms, curated databases, and cutting-edge computational biology. This technology optimizes therapeutic candidates across diverse modalities such as AAV, lentivirus, and nanoparticles, focusing on safety and efficacy. WhiteLab Genomics supports various applications including gene and RNA-based therapies, target receptor identification, viral vector design, non-viral vector design, payload design, and bioproduction optimization. It aims to significantly accelerate and de-risk drug development processes.

Blazar develops software and methods to assist biomedical and translational R&D teams in maintaining interpretability, traceability, and continuity of results. It addresses the challenge of results losing clarity as data, models, instruments, pipelines, or conditions of use change across biomarker programs, multi-site environments, AI-enabled pathology, and translational workflows. Blazar helps organizations structure these sensitive workflows so that results, model outputs, and supporting evidence remain usable, anchored in context, and followed over time. The platform integrates heterogeneous results into a coherent analytical framework, operates within controlled environments (on-premise, private cloud), and supports human review and oversight in sensitive or multi-stakeholder workflows. It also tracks data lineage and metadata for continuity of interpretation.

Gero is a therapeutics company dedicated to rewriting human aging through the application of physics and AI. The company's mission is to develop drugs that restore resilience, delay disease, and significantly extend healthy human lifespan. Gero's AI platform is trained on over 100 million longitudinal medical records, providing a deep understanding of how human bodies age and recover. Utilizing physics-informed models, it translates this data into a quantitative picture of aging driven by entropy buildup, identifying targets that restore systemic resilience and control disease progression. Gero collaborates with pharmaceutical companies on age-related indications while also advancing anti-aging interventions in-house.

Intelligencia AI revolutionizes drug development by providing AI-driven insights to enhance portfolio strategy, reduce risks, and make informed decisions. The platform helps bridge the gap between scientific excellence and business acumen, offering solutions to measure the likelihood of success for drugs across multiple therapeutic areas. It bolsters internal risk identification with accurate AI-driven assessments, pinpoints underlying drivers of technical and regulatory risk, and helps optimize clinical trial design. Intelligencia AI provides a single source of quality, harmonized data for consistent risk evaluation, ensuring transparency with explainable AI that clarifies the 'how' and 'why' behind its predictions. This allows pharmaceutical and biotech companies to make more confident decisions with trusted AI.

deeplearning-biology is a meticulously curated list of deep learning implementations specifically tailored for biological applications, with a strong emphasis on genomics. This resource compiles various methods, models, and research papers, offering insights into areas such as sequence modeling, multi-omics integration, protein biology, and drug discovery. It serves as an invaluable guide for researchers and developers seeking to understand and apply deep learning techniques to complex biological data. The list includes reviews, model repositories, and specific implementations across diverse biological subfields, highlighting practical issues, tooling, and model explainability.

Pluto Bio offers a collaborative multi-omics platform designed to accelerate research and drug discovery. It provides a unified workspace for preclinical and translational strategy, enabling multi-site, interdisciplinary collaboration in real-time. The platform centralizes data visualization with a no-code canvas, allowing users to explore data and test scientific hypotheses quickly while maintaining end-to-end traceability. Pluto Bio supports a wide range of biological assays, including scRNA-seq, RNA-Seq, ChIP-seq, ATAC-seq, and Spatial Transcriptomics, with pipelines for custom assays. It helps organize experiments, plots, data, and files in a secure cloud environment, facilitating target identification, biomarker discovery, and mechanism tracking.

SDM is a trusted partner specializing in innovative software solutions and services. The company offers technology-driven solutions designed to meet diverse business needs. SDM's expertise spans various IT solutions, aiming to provide comprehensive support and development. Their focus is on delivering high-quality software that drives efficiency and innovation for their clients. With a commitment to technology, SDM helps businesses discover and implement advanced solutions tailored to their specific requirements, ensuring they stay competitive in today's rapidly evolving digital landscape.

DIPY (Diffusion Imaging in Python) is a comprehensive open-source Python library designed for the analysis of MR diffusion imaging and other 3D/4D+ medical images. It provides a robust set of generic methods for tasks such as spatial normalization, signal processing, machine learning, and statistical analysis. Beyond general medical image processing, DIPY specializes in computational anatomy, offering advanced techniques for diffusion, perfusion, and structural imaging. The library is intended for research purposes, with a clear disclaimer for clinical deployment. It supports installation via pip or conda and adheres to Scientific Python SPEC 0 for version compatibility, making it accessible for researchers and developers in the medical imaging field.

Model Medicines is an AI-driven company dedicated to building better medicines by innovating at the intersection of data science, biology, and drug development. The platform utilizes AI to model chemistry and human biology, accelerating the discovery and development of life-changing drugs. With 192 compounds and 67 validated assets in disease-relevant cellular models across 12 therapeutic targets, Model Medicines focuses on areas such as virology, oncology, inflammation, and longevity. Their proprietary GALILEO™ and AmesNet™ technologies enable ultra-large virtual screening and agentic AI breakthroughs, leading to the identification of best-in-class potential therapeutics, such as MDL-001, a direct-acting, broad-spectrum antiviral.

Abzu is a biotechnology company leveraging explainable AI to innovate in the field of RNA therapeutics. The company specializes in developing best-in-class RNA drugs, including siRNAs, ASOs, and anti-miRs, for significant medical needs. Their AI-guided design platform, powered by the QLattice®, allows for the in silico exploration and prioritization of vast sequence spaces, evaluating over 100,000 design variants to predict efficacy and developability properties. This approach significantly reduces experimental cycles, lowers costs, and shortens the time to candidate selection. Abzu also focuses on RNA-based delivery systems, developing targeted aptamers for cell-specific uptake of therapeutic RNA, offering a modular platform for precision delivery beyond the liver. The team combines deep RNA biology, AI-driven design, and drug development experience to create a closed learning loop where data refines models and models improve molecules.

TorchDrug is a robust, PyTorch-based machine learning platform specifically designed for drug discovery. It simplifies the implementation of graph operations in a PyTorchic style with GPU support, making it accessible even for practitioners with minimal drug discovery knowledge. The platform facilitates rapid prototyping of machine learning research by providing a wide range of common datasets and building blocks. Users can easily work with graph-structured data and molecules, extracting properties without deep domain expertise. TorchDrug also accelerates training and inference across multiple CPUs or GPUs, offering seamless scalability for complex experiments. It supports integration with Weights & Biases for experiment tracking and management.

Brightseed is a continuous innovation platform designed for health and life sciences companies. It unifies deep science, cutting-edge AI, and the world’s largest proprietary bioactive dataset to streamline the discovery, validation, and commercialization of new products. The platform helps teams identify and prioritize promising opportunities earlier, reducing guesswork and increasing confidence before significant investments are made. By replacing fragmented R&D workflows with a unified system, Brightseed accelerates progress from discovery to decision, leading to faster cycles, quicker time to market, and a higher probability of success for innovations. It empowers teams to deliver more successful innovations, faster, fueling sustainable growth and long-term differentiation.

AIMEDIC Operator is a B2B AI layer designed for healthcare institutions in Colombia, integrating seamlessly with existing HIS and other data sources like ERPs and analytical warehouses. It automates critical administrative tasks such as generating RIPS (Registro Individual de Prestación de Servicios de Salud), reducing glosas (claim denials) through pre-billing validation, and ensuring regulatory compliance with Colombian health laws like Ley 1581. The platform allows users to query data and generate dashboards using natural language, eliminating the need for SQL or specialized technical knowledge. It focuses on enhancing operational efficiency, providing real-time insights, and adapting to evolving regulatory standards without requiring a replacement of current core systems.

Bexorg, Inc. is at the forefront of CNS drug discovery, utilizing its innovative BrainEx platform to address the root causes of clinical trial failures. This unique laboratory restores molecular activity in donated human brains, providing an intact human brain model for experimental access. By eliminating reliance on oversimplified model systems, Bexorg generates clinically predictive data, accelerating the discovery, validation, and delivery of effective therapies. The platform supports systematic delivery of various therapeutic modalities, including small molecules, biologics, viral vectors, and LNPs, through the competent vascular system and blood-brain barrier. This technology offers unprecedented insights into CNS pharmacology, enabling researchers to advance drug programs with confidence.

CytoReason is an AI platform designed to revolutionize drug development by leveraging comprehensive computational disease models. It moves beyond traditional trial-and-error methods, offering a predictable approach to understanding human diseases at a granular level. The platform allows users to model and compare various treatments and patient groups, providing a clear map of disease and drug mechanisms. This enables pharmaceutical companies to manage their drug portfolios effectively, prioritize programs, and compare their treatments against competitors. CytoReason provides both deep molecular insights for scientists and a high-level overview for executives, ensuring all stakeholders can make data-driven decisions based on continuously updated disease models and live results.

Loon offers regulatory-compliant agentic AI systems specifically designed for Healthcare Technology Assessment (HTA), Health Economics and Outcomes Research (HEOR), and Market Access. The platform automates systematic literature reviews (SLR) and indirect treatment comparisons (ITC) for evidence synthesis, helping to optimize value dossiers. By forecasting payer decisions, Loon aims to de-risk reimbursement processes for pharmaceutical and biotech companies. Its AI capabilities are geared towards accelerating market access and streamlining the complex landscape of drug reimbursement, providing a robust solution for healthcare professionals navigating regulatory and commercial challenges.

Immunai is an advanced AI tool dedicated to decoding the immune system, offering solutions for drug discovery and development. It partners with biopharmaceutical companies and research institutions to identify novel targets, prioritize drug candidates, and optimize clinical trials. The platform transforms complex therapeutic questions into actionable recommendations by generating high-quality, multiomic data, augmenting it with AMICA (the world's largest immune-focused single-cell database), and leveraging advanced machine learning to compute novel immune features. Immunai validates ML-driven hypotheses through functional genomics, providing clear, actionable paths for decision-making in drug development.

Purposeful is a biotech company dedicated to discovering new treatments and developing pharmaceutical products for rare diseases. The company leverages AI to repurpose already marketed drugs, aiming to reduce the high costs and lengthy timelines typically associated with drug development. By integrating AI with human expertise, Purposeful seeks to identify additional therapeutic effects of existing compounds and revisit both rare and common diseases to reverse disease phenotypes. Their vision is to fully employ AI repurposing to address a wide range of conditions, including SYNGAP1-related intellectual disability, Pitt-Hopkins Syndrome, Fragile X Syndrome, Glioblastoma, Psoriasis, Duchenne Muscular Dystrophy, Huntington’s disease, and Acrodysostosis.

Valence Labs, powered by Recursion, functions as an AI research engine dedicated to accelerating the mission of decoding biology to radically improve lives. The platform utilizes Recursion’s extensive data generation capabilities, including over 60 petabytes of phenomics and transcriptomics data, alongside its BioHive supercomputer for massive computing power. Valence Labs focuses on predicting cellular responses to perturbations, explaining molecular interactions, and discovering novel therapeutic hypotheses through lab-in-the-loop engines. This approach aims to build 'virtual cells'—mechanistic models of cellular function—to guide the discovery of new therapeutics and improve drug discovery outcomes.

Cortical Labs pioneers a new era of computing by merging lab-grown neurons with silicon chips, creating what they term 'actual intelligence.' Their flagship product, the CL1, is the world’s first code-deployable biological computer, allowing users to test how lab-grown neurons process information and learn. Complementing this, the Cortical Cloud provides a biological cloud computing platform, enabling users to build breakthrough technology without needing a specialized lab. This innovative approach leverages real neurons' intuitive learning capabilities and remarkable efficiency, requiring minimal energy and training data compared to traditional AI. The technology aims to revolutionize fields like healthcare discoveries, personalized medicine, and enhanced cell therapies by solving profound problems with biocomputing's energy efficiency.

Bio Logbook is an innovative deep-tech startup founded in 2018, specializing in AI-driven software solutions for biological data analysis. The platform offers three core products: Bio Logbook Healthcare, a medical decision support software for professionals to detect biological imbalances and estimate unmeasured parameters; Bio Logbook Connect, an application for patients to easily understand their biological results through interactive dashboards and clear explanations; and Bio Logbook Services, a solution for optimizing clinical research studies. The tool aims to improve diagnostic precision, anticipate chronic diseases, and enhance patient care by providing optimized, precise, and actionable biological analyses. It holds certifications like DM-DIV for Bio Logbook Healthcare and HDS for data protection, ensuring reliability and security.

Gensaic is at the forefront of metabolic medicine, utilizing AI-enabled protein design to revolutionize therapeutic delivery. Their FORGE™ Platform employs Functional Optimization by Recursive Genetic Evolution, combining billion-scale protein evolution with AI-guided design to uncover novel mechanisms for tissue-selective intracellular delivery. This approach allows for the unbiased discovery of protein sequences that enable precise delivery to target tissues, specific cell types, and even subcellular compartments. Gensaic's focus is on developing potent, selective, and safe medicines for a broad range of metabolic indications, accelerating the discovery of next-generation therapies.

BioRaptor is a bioprocessing data analytics platform designed to streamline data management and analysis for bioprocessing teams. It automatically collects, organizes, and contextualizes bioprocess data in one central location, enabling instant cross-run analysis and real-time monitoring. The platform helps users identify contributing factors, perform root cause analysis, and accelerate process development and optimization. BioRaptor offers automated reports, graphs, and dashboards for easy visualization and communication. It is built specifically for biological data, supporting time-series measurements and integrating with various instruments. The platform also includes machine learning capabilities for building software-based sensors and predictive models without coding, and boasts a rapid implementation time of 2-4 weeks.