MedFacts provides an AI‑driven platform that generates novel molecular structures from user‑defined scaffolds and evaluates them for drug‑likeness, bioactivity, ADME, and target docking. The system uses generative deep‑learning models and predictive neural networks to rank chemically feasible candidates, helping pharmaceutical R&D and biotech teams accelerate lead identification and reduce wet‑lab screening costs.
Funding
$50K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Early-stage drug discovery relies on labor-intensive synthesis and screening of large compound libraries, leading to long timelines and high costs while many candidates fail due to poor drug‑likeness, bioactivity, or ADME properties.
Solution
MedFacts offers an AI‑driven platform that generates novel molecular structures from user‑defined scaffolds and evaluates them for drug‑likeness, bioactivity, ADME, and target docking. The system uses generative deep‑learning models to propose chemically feasible candidates that have not been reported in prior studies. Integrated predictive neural networks estimate permeability, solubility, toxicity, and inhibitory activity against specific protein targets, allowing researchers to prioritize the most promising leads. By automating design and in‑silico assessment, MedFacts shortens the lead‑identification phase and reduces the need for extensive wet‑lab screening.
Target Audience
Primary customers are pharmaceutical R&D teams and biotech companies seeking to accelerate early‑stage lead generation, as well as contract research organizations that provide drug discovery services.
Features
- Generative AI engine that creates novel molecules from custom scaffolds, expanding chemical space beyond existing libraries
- Multi‑parameter drug‑likeness scoring that filters for safety, plausibility, and synthetic accessibility
- Predictive models for bioactivity across organisms, human cell lines, and key pharmacokinetic properties (permeability, solubility, toxicology, ADME)
- Target‑driven docking predictions and inhibitory activity estimates using deep‑learning‑based molecular docking
- Integrated workflow that ranks candidates for rapid selection and downstream experimental validation