AI began entering biomedical research in the 1970s through early initiatives like SUMEX, which explored how computing could support scientific reasoning. By the late 1980s, applications such as MULTICASE and ADAPT introduced AI-driven tools for toxicity prediction, marking its first use cases in drug development.
By 2022, AI-generated drug candidates began entering preclinical and clinical pipelines, moving from theory to application. COVID-19 accelerated interest in computational methods, and the release of AlphaFold showed how AI could crack complex biological prediction problems. Around the same time, transformer-based large language models—popularized by tools like ChatGPT—made AI more visible across research and industry.
Regulatory agencies are responding as well—in 2024, the FDA’s Center for Drug Evaluation and Research (CDER) formed an AI council and released draft guidance on AI use in drug development workflows.
In this article: Protein and Peptide Engineering Platforms — Model Simulation and Physics‑Based Tools — Generative and Foundation Model Ecosystems — AI‑Enhanced Drug Discovery Workflows — Multi‑Omics and Precision Diagnostics — AI‑Driven R&D Support and Optimization Platforms — Autonomous Scientific Research and Lab Automation
Today we’re taking a look at a new wave of startups that entered the AI drug discovery scene over the past two years, between 2023 and early 2025.