Building AI models that understand chemical principles

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.

Inverse Design of Inorganic Compounds with Generative AI

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

Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering

The article discusses how the rapid development of artificial intelligence (AI) and machine learning (ML) is transforming chemical engineering by influencing problem formulation, analysis, and solution across a wide range of applications, from atomic-scale simulations to industrial operations. It highlights recent methodological advances and representative uses, noting a shift from purely black-box models to hybrid and physics-informed frameworks that incorporate conservation laws, thermodynamic consistency, and structural constraints. These integrated approaches enhance robustness, reliability, and human-AI collaboration, ultimately amplifying rather than replacing core chemical engineering principles.

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.

Chem World: A Large-Scale Benchmark and Physics-Informed Framework for Trustworthy Chemical Property Prediction

arXiv:2607. 28079v1 Announce Type: new Abstract: Chemical property prediction plays a critical role in accelerating scientific discovery in chemistry, materials science, and drug development.

NMR Elucidation as an Agentic Search Problem, Not a Modeling Problem

arXiv:2607. 19406v1 Announce Type: new Abstract: Structural elucidation from Nuclear Magnetic Resonance (NMR) data remains a fundamental bottleneck across chemistry, materials science, and biology.

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.

ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification Across the Periodic Table

arXiv:2606. 30961v1 Announce Type: cross Abstract: Advances in deep learning architectures and representations have enabled ML-driven chemical property prediction, but state-of-the-art (SOTA) models have remained largely confined to independent codebases and lack support for diverse chemical species.

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

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.

MolLedger: An Additive Graph Neural Network with Chemically Grounded ADME Attributions

arXiv:2608.30636v1 Announce Type: new Abstract: Optimizing absorption, distribution, metabolism, and excretion (ADME) is an important part of small molecule drug discovery. Many machine learning mode...

A General-Purpose Framework for Chemical Reaction Representation with Atomic Correspondence and Flexible Condition Adaptation

The paper introduces Align-React, a chemical reaction representation learning framework that incorporates atomic correspondence between reactants and products, an adapter for embedding reaction conditions, and a Reaction-Center-Aware attention mechanism. These components enable the model to capture precise molecular transformations and focus on critical functional groups, leading to improved performance across a variety of organic reaction tasks. The framework outperforms existing architectures on most benchmark datasets.