OpenAI Blog
Jun 17
A near-autonomous AI chemist improves a challenging reaction in medicinal chemistry
OpenAI and Molecule. one show how a near-autonomous AI chemist using GPT-5.
SAIR: Accelerating Pharma R&D with AI-Powered Structural Intelligence
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MIT News AI
May 20
Building AI models that understand chemical principles
Connor Coley works at the interface of chemistry and machine learning, to discover and design new drug compounds.
arXiv AI
Jun 18
TxBench-PP: Analyzing AI Agent Performance on Small-Molecule Preclinical Pharmacology
arXiv:2606. 19245v1 Announce Type: new Abstract: Artificial intelligence (AI) agents promise to accelerate drug discovery by compressing interpretation and decision-making loops, but practical deployment requires trusted evaluation on realistic program decisions.
arXiv AI
Aug 25
RetroDFM-R: Reasoning-Driven Retrosynthesis Prediction with Large Language Models via Reinforcement Learning
arXiv:2507.17448v2 Announce Type: replace-cross Abstract: Retrosynthetic planning is a cornerstone of organic synthesis and drug discovery. Yet existing AI methods often rely on pattern matching rath...
arXiv AI
Jun 30
Accelerating scientific discovery with Co-Scientist
arXiv:2502. 18864v2 Announce Type: replace Abstract: Scientific discovery is driven by scientists generating novel hypotheses for complex problems that undergo rigorous experimental validation.
arXiv AI
1d ago
R-GroundBench: A Diagnostic Benchmark for R-Group Groundingin Markush Molecular Editing
R-GroundBench is a new diagnostic benchmark for evaluating AI models on R‑group grounding in Markush molecular editing, derived from real pharmaceutical patents. It includes a Multiple‑Choice VQA track with varying difficulty and modality splits, as well as an open‑ended Generation track. Experiments show a large performance gap: models score over 90% on easy VQA but drop to 56–66% on hard VQA, and generation exact match stays below 20% (and under 8% with visual input).
arXiv AI
Aug 10
Strategy-first synthesis planning for complex natural products
arXiv:2608. 07454v1 Announce Type: cross Abstract: The total synthesis of a complex molecule is among the most demanding intellectual and experimental feats in chemistry: a chemist must plan many steps ahead for how to assemble simple building blocks into an intricate target, devise backup strategies, and anticipate procedural challenges.
arXiv AI
Jul 7
Folding, Reasoning, and Scaling with Open-source Drug Discovery Engine
arXiv:2607. 03787v1 Announce Type: new Abstract: Accurately modeling biomolecular interactions is a central bottleneck in biology and therapeutic discovery.
arXiv AI
Aug 20
Science Done on a Machine by a Machine: AI Agents in Computational Chemistry
The Perspective reviews the rapid growth of agentic AI systems in computational chemistry, noting an increase from a handful in 2024 to about fifty by August 2026. These systems are evolving from assisting with specific tasks to autonomously designing, executing, and analyzing in‑silico experiments, even drafting manuscripts. While fully autonomous AI scientists are not yet realized and human oversight remains, the trend toward commoditized generalist agents suggests a future where specialized systems may become obsolete, prompting reflection on the field’s direction and priorities.
arXiv AI
Jul 24
Leveraging Biokinetic Knowledge Priors for Data-Scarce Bioprocess Modeling
arXiv:2607. 20539v1 Announce Type: cross Abstract: While deep learning has accelerated drug discovery, its impact on biomanufacturing has been considerably more limited.
arXiv AI
Aug 13
A Modular Agentic Framework for Synthetically Constrained Multi-Objective Hit-to-Lead Optimization
arXiv:2608. 11483v1 Announce Type: new Abstract: Hit-to-lead optimization requires iterative design of hit analogs across competing potency, selectivity, physicochemical, pharmacokinetic, safety, and synthetic constraints.
arXiv AI
Sep 10
The convergent laboratory: when AI reasoning, autonomous experiments, high performance and quantum computing reshape chemistry
The article titled "The convergent laboratory: when AI reasoning, autonomous experiments, high performance and quantum computing reshape chemistry" discusses insights from the TPC26 conference, where leaders from academia, national laboratories, and industry examined how AI, autonomous agents, self-driving labs, high‑performance computing, and quantum computing converge to accelerate materials science discovery. It presents firsthand experiences from researchers at the forefront of these technologies and argues that their simultaneous maturation marks a tipping point for transformative advances and productive disruption in chemical sciences.