+ Equity-based compensation
More locations: San Francisco, CA, USA | New York, NY, USA | United States
Hybrid work is required; the role is listed in New York City, San Francisco, or Boston.
Inductive Bio uses machine learning on a proprietary, curated dataset to help pharmaceutical, biotech, and research teams design small molecule drugs. Its platform provides real-time ADMET predictions and data-driven insights to guide drug design throughout development. The system stands out by pairing a thoroughly curated ADMET dataset with ML models in a user-friendly platform that supports entire teams, not just data scientists. The goal is to accelerate the discovery and optimization of safe, effective small molecule drugs by delivering practical, data-driven guidance during development.
Company Size
11-50
Company Stage
Series A
Total Funding
$29.3M
Headquarters
New York City, New York
Founded
2021
See people who can refer or advise you
Help us improve and share your feedback! Did you find this helpful?
Health Insurance
Dental Insurance
Vision Insurance
Commuter Benefits
Phone/Internet Stipend
Home Office Stipend
Flexible Work Hours
Remote Work Options
Paid Vacation
Paid Sick Leave
Paid Holidays
Unlimited Paid Time Off
401(k) Retirement Plan
401(k) Company Match
Company Equity
Stock Options
Wellness Program
Mental Health Support
Professional Development Budget
Conference Attendance Budget
Tuition Reimbursement
Professional Certification Support
Training Programs
Pet Insurance
Adoption Assistance
Family Planning Benefits
Fertility Treatment Support
Parental Leave
Relocation Assistance
Employee Discounts
Meal Benefits
Gym Membership
Inductive has launched Beacon-2, a system that predicts the efficacious human dose of a small molecule directly from its chemical structure. The tool enables chemists to estimate which compounds will have the best human dose before laboratory synthesis. Beacon-2 predicts absorption, distribution, metabolism, excretion, and toxicity properties, then combines them through mechanistic pharmacokinetic models. Its underlying ADMET models have won three consecutive OpenADMET blind challenges, beating more than 750 competitors from leading AI and pharmaceutical companies. In testing, Inductive's medicinal chemistry agent used Beacon-2 to improve predicted human dose by 17 times over five autonomous design cycles on a SARS-CoV-2 compound. The system is available to partners through Compass and is already running on live drug discovery programmes. Inductive's models support over 100 discovery programmes with leading biopharma partners.
Inductive Bio has launched Indy, an AI chemistry assistant designed to enhance medicinal chemists' productivity. The company claims Indy has doubled its team's efficiency in partner programmes. Indy performs tasks including quality-checking assay data, analysing structure-activity relationships, and designing molecule analogues. In a published comparison, Indy achieved 89% accuracy on 84 dose-response curves from real programmes, outperforming Claude Opus 5 (48%) and GPT-5.6 Sol (39%). The AI assistant handles routine tasks such as parsing contract research organisation reports, quality-controlling dose-response curves, running generative chemistry algorithms, and creating project update presentations. This allows scientists more time for high-impact work. "Indy surfaces patterns in the data that help us make better decisions," said Nicholas Perl, head of chemistry at Anvia Therapeutics. The tool is now available to Inductive Bio's partners.
Inductive Bio has joined Anthropic's life sciences ecosystem, launching a Model Context Protocol connector that integrates its ADMET prediction models directly into Claude. The integration allows drug discovery scientists to access state-of-the-art predictions for Absorption, Distribution, Metabolism, Excretion and Toxicity properties through natural language queries within Claude's interface. ADMET prediction addresses a major bottleneck in small molecule drug discovery. Inductive Bio's models placed first among over 370 entries from pharmaceutical and AI companies in the OpenADMET-ExpansionRx blind challenge. The connector enables scientists to explore chemical structures and reason about predictions whilst maintaining data protection—submitted structures are not retained or used for model training. Inductive Bio currently powers dozens of active discovery programmes with biopharma partners.
Team to develop and validate computational models trained on data from organoids and advanced human model systems that will improve drug safety and reduce reliance on animal testing
Inductive Bio has secured up to $21 million in funding to develop AI-driven drug toxicity models that could transform safety assessments in drug discovery. The initiative, called DATAMAP, involves collaboration with Amgen, Cincinnati Children's Hospital Medical Center, Baylor College of Medicine and Torch Bio. The project aims to reduce reliance on animal testing by using human-derived data from organoid systems to predict drug safety more accurately. Currently, nearly 90% of clinical-stage drug candidates fail to reach market, with 25% failing due to unforeseen safety issues. The initial phase will focus on drug-induced liver injury and cardiotoxicity, which account for over 40% of drug withdrawals. Inductive Bio will work with the FDA to validate these AI models for regulatory approval, aligning with the agency's push to modernise drug development methodologies.