A near-autonomous AI chemist improves a challenging reaction in medicinal chemistry

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.

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.

Measuring AI’s capability to accelerate biological research

OpenAI introduces a real-world evaluation framework to measure how AI can accelerate biological research in the wet lab. Using GPT-5 to optimize a molecular cloning protocol, the work explores both the promise and risks of AI-assisted experimentation.

SAIR: Accelerating Pharma R&D with AI-Powered Structural Intelligence

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.

Early experiments in accelerating science with GPT-5

OpenAI introduces the first research cases showing how GPT-5 accelerates scientific progress across math, physics, biology, and computer science. Explore how AI and researchers collaborate to generate proofs, uncover new insights, and reshape the pace of discovery.

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.

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.

Inverse Design of Inorganic Compounds with Generative AI

arXiv:2604. 11827v2 Announce Type: replace-cross Abstract: Machine learning is revolutionizing chemistry.

Evaluating AI’s ability to perform scientific research tasks

OpenAI introduces FrontierScience, a benchmark testing AI reasoning in physics, chemistry, and biology to measure progress toward real scientific research.

Molecular LLM Agents: From Architectural Design to Scientific Autonomy

arXiv:2608.23104v1 Announce Type: cross Abstract: Molecular science represents an important frontier for LLM-based agents. Unlike general agents that mainly operate over natural language, code, or we...

Introducing GPT-Rosalind for life sciences research

OpenAI introduces GPT-Rosalind, a frontier reasoning model built to accelerate drug discovery, genomics analysis, protein reasoning, and scientific research workflows.