Innovations in AI-powered drug discovery enable faster and more efficient drug development. For instance, machine learning algorithms analyze vast amounts of biological data and identify potential drug candidates. These algorithms also identify patterns and relationships in data that are difficult for humans to detect, enabling more precise and targeted drug development. Another recent innovation in AI-powered drug discovery is the use of virtual screening tools that simulate the interaction between potential drug candidates and target molecules. This method reduces the time and costs associated with traditional drug discovery methods. Additionally, AI-powered clinical trials allow pharma companies to collect and analyze patient data more efficiently and accurately. This approach helps them identify patient subgroups that may benefit from a particular drug, enabling more personalized medicine approaches. Dive into our curated list of 20 AI startups advancing drug discovery in 2025 and beyond.
This article was last updated in July 2024.
20 AI Startups advancing Drug Discovery in 2025
- Seismic Therapeutic – Immunology Drug Discovery
- Pathos – AI-Driven Precision Medicine
- Prescience Insilico – Synthetic Molecule Generator
- Araceli Biosciences – AI-based Drug Screening
- April19 Discovery – AI-driven Psychedelic Drug Discovery
- Skymount Medical – Rapid Drug Discovery
- Aimble – Molecular Dynamic Drug Screening
- TK Analytics – Advanced Cell Painting Analysis
- Eleven Therapeutics – Oligonucleotide Therapeutics
- QuantHealth – Clinical Trial Simulations
- Ailynix – Convolutional Graph Networks for Drug Design
- Pangea Botanica – Novel Small Molecule Therapeutics
- DevsHealth – Molecular Modeling
- Vevo Therapeutics – In Vivo Guided Drug Discovery
- Gandeeva Therapeutics – Protein-Drug Interactions
- Cortex Discovery – Molecular Dynamics
- CardiaTec Biosciences – Cardiovascular Disease Drug Targets
- Boltzmann Labs – Novel Small Molecule Discovery
- molab.ai – Absorption, Distribution, Metabolism, Excretion, & Toxicity (ADMET) Prediction
- CarbonSilicon – Druggability Prediction
Global Startup Heat Map highlights 20 AI Startups advancing Drug Discovery
Through the Big Data & Artificial Intelligence (AI)-powered StartUs Insights Discovery Platform, which covers over 4.7M+ startups & scaleups globally, we identified 463 AI startups advancing drug discovery. The Global Startup Heat Map below highlights the 20 AI-based drug discovery startups you should watch in 2025 as well as the geo-distribution of all 463 startups & scaleups we analyzed for this research.
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Based on the heat map, we see high startup activity in the USA, followed by Western Europe. These AI-based drug discovery startups work on solutions ranging from convolutional graph networks to molecular modeling and protein-drug interactions to agile drug development.
As the world’s largest resource for data on emerging companies, the SaaS platform enables you to identify relevant technologies and industry trends quickly & exhaustively. Based on the data from the platform, the Top 5 AI-powered Drug Discovery Startup Hubs are in London, New York City, Cambridge, Boston & San Francisco.
The 20 hand-picked startups highlighted in this report are chosen from all over the world and develop solutions for small molecule therapeutics, in vivo guided discovery, druggability prediction, and more.
Explore 20 AI Startups advancing Drug Discovery (2025)
Seismic Therapeutic advances Immunology Drug Discovery
Seismic Therapeutics is a USA-based startup that specializes in immunology drug discovery through machine learning. Its IMPACT platform integrates machine learning with structural biology, protein engineering, and translational immunology, optimizing multiple drug-like properties simultaneously. The platform explores new protein sequences, enabling the rapid development of novel biologics with minimized immunogenicity.
Seismic Therapeutics also applies the technology to create specialized enzymes and antibodies, addressing autoimmune diseases. The startup’s solutions include sculpting immunoglobulin enzymes and developing dual-cell bidirectional antibodies, enhancing drug properties. Moreover, Seismic Therapeutics parallelized multi-property optimization and machine learning significantly accelerate the drug discovery process.
Pathos enhances AI-Driven Precision Medicine
USA-based startup Pathos develops a platform that utilizes vast oncology data, genomic information, and advanced AI with laboratory environments to re-engineer drug development. The PathOS platform integrates real-world oncology data, patient-derived functional genomic data, and silicon analyses with biological modeling to design optimal clinical trials.
The startup’s clinical development suite also harmonizes data from multiple sources, enabling a generalizable machine-learning framework for asset-specific analytics and AI models. Furthermore, Pathos’ Discovery Engine automates target identification and prioritization, refining targets using advanced AI and vast patient data, accelerating the development of precision medicines.
Prescience Insilico makes a Synthetic Molecule Generator
Prescience Insilico is an Indian startup that advances in silico drug development with its PRinS3 platform. The platform integrates advanced algorithms for high-throughput virtual screening, enabling efficient drug discovery processes. Its Artificial Intelligence-Synthetic Molecule Generator (SyMoG/AI) contains 7 pre-trained models for drug targets like Kinase, Nuclear Receptor, etc., and uses a graphical neural network-based AI model.
Besides this, PRinS3’s X-ESS feature automates screening with free energy of binding calculations while supporting multi-target, multi-ligand screening, and utilizing cloud and in-house servers for computational tasks. Prescience Insilico’s approach significantly accelerates the optimization, screening, and evaluation phases in target-based drug development.
Araceli Biosciences provides AI-based Drug Screening
Araceli Biosciences is a USA-based startup that creates a drug discovery system Voyager that employs deep learning-powered neural networks for precise segmentation in challenging assays. The system features a user-friendly interface, enabling efficient interaction with images and measurements with visualization tools like heatmaps and scatter plots.
The system facilitates data analysis and trend identification and also integrates with Araceli’s Endeavor and EndeavorGT to enhance data quality and throughput. Furthermore, Voyager’s machine learning AI minimizes variability, providing reliable data for informed decision-making and serves to streamline the high content screening process.
April19 Discovery promotes AI-driven Psychedelic Drug Discovery
April19 Discovery is a UK-based startup that focuses on AI-driven psychedelic drug discovery with computational chemistry to design and develop next-generation psychedelic-inspired compounds. The startup employs advanced computational drug design and organic synthesis to yield drug candidates which also allows for the exploration of a vast chemical space.
Its drug design platform contains a suite of AI-driven computational models and tools that can screen billion-compound libraries, generate novel targeted molecules, and predict complex molecular properties. April 19’s technology combines AI, computational chemistry, and psychedelic drug design to advance therapeutics for cognitive decline and dementia.
Skymount Medical advances Rapid Drug Discovery
USA-based startup Skymount Medical accelerates drug discovery through its platform DeepDrug which uses AI-driven methods to significantly reduce long R&D cycles. DeepDrug utilizes computational techniques to streamline drug discovery with AI algorithms analyzing vast chemical spaces, and identifying potential drug candidates rapidly. Besides this, the AI analyzes protein-protein interactions to map known antiviral peptides to cell mechanisms.
Skymount Medical’s platform also includes a Toxicity Filter that quantifies risk, a Modelcule Engine that decomposes existing drugs, and a Hypergraph Engine that simulates the most effective compounds. The startup’s platform assists in predicting drug efficacy and safety and serves as a vital tool for researchers and pharmaceutical companies, expediting the journey from lab to market.
Aimble enables Molecular Dynamic Drug Screening
South Korean startup Aimble enhances drug discovery through its AI-based drug screening platform that integrates molecular dynamics with AI to automate the process. The platform’s AI algorithms analyze extensive biomedical data to identify potential drug candidates with a machine-learning engine that predicts drug potency based on binding affinity calculations.
Moreover, Aimble’s high-speed docking solution uses a distributed computer system to reduce calculation time by simultaneously docking multiple compounds. The platform also features AI-based binding force and toxicity prediction models to streamline the early stages of drug discovery, particularly in identifying compounds for challenging diseases.
TK Analytics provides Advanced Cell Painting Analysis
Slovenian startup TK Analytics develops an advanced cell painting analysis platform CellusAI that refines and accelerates drug discovery using detailed information from cell painting images. The platform offers image segmentation, phenotyping, and prediction of molecular bioactivity and toxicity with a New Molecule Generative AI module, which generates chemical structures to induce specific cell painting profiles.
CellusAI not only streamlines image segmentation and profile extraction but also enriches data with public and private datasets to improve phenotypic screening in drug discovery. In this way, TK Analytics enables researchers to manage imaging data flow, analyze and organize it, and make the data accessible when needed.
Eleven Therapeutics improves Oligonucleotide Therapeutics
UK-based startup Eleven Therapeutics simplifies RNAi drug development into a programmable process. The startup’s platform TERÅ beads features combinatorial chemistry, AI, and massively parallel processes to map the chemical space and reveal the structure-activity relationship of RNA molecules. Additionally, through big data, Eleven Therapeutics optimizes every component of RNA molecules, including safety, efficiency, and duration of effect.
Besides this, TERÅ beads also utilize deep learning to understand the effects of chemical modifications on RNA oligonucleotides. The split-pool process creates millions of TERÅ beads, each with a unique chemically modified RNA oligo and a DNA barcode. This process allows for variability in desired positions along the therapeutic RNA molecules. Through this technology, Eleven Therapeutics’ can perform chemical modification with optimal patterns to optimize bioavailability, reduce immunogenicity, and mitigate off-target effects.
QuantHealth simplifies Clinical Trial Simulations
Israeli startup QuantHealth provides a platform for simulating clinical trials to accelerate and de-risk drug development. Their In-Silico platform generates synthetic evidence for therapy performance across all clinical phases. This synthetic evidence-generation engine supports trial planning, indication selection, drug repurposing, and in-licensing asset evaluation.
QuantHealth’s Large Healthcare Model (LHM) learns deep patient-drug interactions, driving high-resolution outcome models with high endpoint accuracy. Moreover, its models continually adapt to new research and integrate with internal trial data and proprietary research. The AI solution enables clinical development and operations teams to simulate a clinical trial in thousands of variations in a short duration.
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Ailynix develops Convolutional Graph Networks (CGNs) for Drug Design
US-based startup Ailynix specializes in AI-based drug design and discovery that leverages deep learning and convolutional graph networks. The startup’s platforms use supervised training methods to develop quantitative structure-activity relationship (QSAR)-based computational models for predicting chemical structures.
They identify potential drugs from a massive molecule database, enabling further computational searches and refinement. The startup’s platforms thus advance protein-based therapeutic drug discovery using orthosteric, allosteric, and functional data. Consequently, they cater to biotech and pharma companies, contract research organizations (CROs), and university research labs.
Pangea Botanica offers Novel Small Molecule Therapeutics
UK-based startup Pangea Botanica develops PangeAI, an AI-powered platform to accelerate drug discovery and development. It maps the chemical composition of plants and creates a diverse dataset of natural products to match compounds. The platform also predicts chemical properties, modes of action, and synergistic effects. By combining AI, metabolomics, and cheminformatics, PangeAI enables the scalable discovery of nature-inspired therapeutics. It also assists in the proposal of lead candidates for development, thereby enhancing the development of new and existing compounds.
DevsHealth advances Molecular Modeling
Spanish startup DevsHealth makes antiviral and antibiotic treatments using AI and molecular modeling. The startup’s AI optimizes drug design, predicts side effects, and forecasts ADME properties. Additionally, it integrates public-source databases to simplify data handling for massive datasets of gene expression experiments, bioactive compounds, and proteins. Further, DevsHealth leverages real-world data (RWD) and quantum computing to enhance its AI models and predictions, enabling better anti-infective treatments.
Vevo Therapeutics enables In Vivo Guided Drug Discovery
US-based startup Vevo Therapeutics creates Mosaic, a platform to generate high-resolution, single-cell in vivo data at scale. It measures both phenotypic and transcriptomic changes in cell states to capture general rules of drug efficacy as well as drug-induced changes in gene expression. The platform also utilizes proprietary methods for pooling cells from multiple patients in one tumor and single-cell RNA profiling to determine drug action. Mosaic also studies drug-cell interactions in vivo, uncovering previously undetectable mechanisms of action and resistance that current in vitro models overlook.
Gandeeva Therapeutics captures Protein-Drug Interactions
Canadian startup Gandeeva Therapeutics offers a drug discovery platform that leverages AI and cryogenic electron microscopy. Its modules include SPOTLIGHT to identify validated targets, HYPERFOCUS which maps druggable sites, and CRYO-CADD which generates structural insights. The platform combines technologies in chemistry, biology, imaging, and machine learning to visualize, measure, and capture protein-protein and protein-drug interactions at high speed and resolution. As a result, the startup accelerates drug discovery by targeting disease-relevant proteins and influencing their function.
Cortex Discovery advances Molecular Dynamics
Cortex Discovery is a German startup that provides deep learning-based solutions to make accurate simulations of ligand-protein binding. The startup’s technology models the chemical processes of interactions between targets and drug-like molecules. This allows for the generalization of a wider range of compound classes and new targets without existing experimental data. Moreover, Cortex Discovery’s technology predicts on-target interactions (hit discovery), off-target interactions (polypharmacology), and drug metabolism and toxicity (ADMET profiling). Consequently, it finds applications in the discovery of drugs for life extension and age-related disorders.
CardiaTec Biosciences offers Cardiovascular Disease Drug Targets
UK-based startup CardiaTec Biosciences applies AI and large-scale multi-omics data to develop cardiovascular disease drug targets. The startup’s AI-driven multi-omics analysis platform uncovers relationships across various biological layers, including gene variation, methylation, expression, proteomics, and metabolomics. The platform also features patient stratification and biomarker identification to facilitate the transition towards personalized medicine, improving patient care.
Boltzmann Labs aids Novel Small Molecule Discovery
Indian startup Boltzmann Labs makes a chemistry studio for discovering novel small molecules and exploring chemical spaces with generative AI. The startup’s studio, BoltChem, creates QSAR property models using machine learning and deep learning. Its AI-based synthesis planning tool, ReBolt, also simplifies reaction pathway design. Additionally, the startup’s BoltBio, a target identification platform, utilizes multi-omics analysis, knowledge graphs, and neural networks to accelerate treatment for rare and common diseases.
molab.ai creates an ADMET Prediction Engine
German startup molab.ai advances drug and compound discovery through ADMET predictions and a compound optimization suite. The startup’s prediction engine provides highly accurate ADMET property predictions with reliable confidence indicators and actionable recommendations for novel molecules. The engine also performs robustly in unfamiliar chemical space, outperforming physics-based models and other AI solutions. Besides, the compound optimization suite offers suggestions for novel and alternative molecular structures optimal for better binding affinity and synthetic accessibility.
CarbonSilicon facilitates Druggability Prediction
Chinese startup CarbonSilicon offers a drug discovery workflow leveraging AI-generated content (AIGC), self-supervised pre-training, reinforcement learning, and physics-based modeling. The startup’s activity prediction solution, Inno-Docking, provides complete protein preparation, ligand preparation, and intelligent setting of docking parameters.
Additionally, Inno-Rescoring features AI-scoring functions to evaluate protein-ligand binding affinity. CarbonSilicon’s comprehensive druggability assessment involves three computational modules Inno-ADMET for ADMET properties, ChemFH to filter frequent hit compounds, and Inno-SA to predict substructure-related toxicity. The startup’s solutions thus enable medicinal chemists to find potential drug candidates more efficiently and easily.
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