Chem ML/AI/Datasets

Computer vision for high-throughput materials synthesis: a tutorial for experimentalists🔥 https://doi.org/10.1039/D5DD00384A Here, we aim to fill that identified gap and present a structured tutorial for experimentalists to integrate computer vision into high-throughput materials research, providing a detailed roadmap from data collection to model validation. Specifically, we describe the hardware and software stack required for deploying CV in materials characterization, including image acquis...

Automated DFT-Machine Learning Integration Enables Data-Efficient and Generalizable Feasibility Predictions in Metallaphotoredox sp2–sp3 Cross-Coupling Reactions https://doi.org/10.1021/acscatal.5c07857 Nickel/photoredox catalysis in cross-coupling reactions offers mild operating conditions for efficient C–C bond formation, expanding synthetic access to pharmaceutically relevant molecules. However, routine implementation of such reactions remains constrained by the intricate reaction mechanism a...

Machine Learning for Green Solvents: Assessment, Selection and Substitution 🔥 https://doi.org/10.1002/advs.202516851 A data-driven pipeline is presented for assessing the sustainability of solvents and identifying greener substitutes. Three models are trained and evaluated on the GlaxoSmithKline Solvent Sustainability Guide (GSK SSG) to predict “greenness” metrics: a traditional Gaussian Process Regression (GPR) model, a fine-tuned GPT model (FT GPT), and a GPT model using in-context learning (...

QSAR Prediction of BBB Permeability Based on Machine Learning upon PETBD: A Novel Data Set of PET Tracers https://pubs.acs.org/doi/10.1021/acs.jmedchem.5c01791 Assessing small-molecule blood–brain barrier permeability is laborious, yet critical in drug development. Quantitative prediction models are hindered by a lack of high-quality data set. To address this, we curated PETBD, a novel data set of drug concentrations for 1056 positron emission tomography tracers across 14 organs at 60 min post i...

Synthetic Applicability Domain (SynAD): Navigating Chemical Space for Reliable AI-Driven Reaction Prediction https://doi.org/10.1002/anie.202523874 Organic synthetic chemistry has undergone a paradigm shift driven by breakthroughs in artificial intelligence (AI). Data-driven methods help accelerate hypothesis evaluation and reduce experimental trial-and-error efforts. However, its practical utility is constrained by the out-of-distribution (OOD) issue, where predictions usually fail when extrapo...

Explainable artificial intelligence for molecular design in pharmaceutical research🔥 https://doi.org/10.1039/D5SC08461J In this Perspective, we examine current challenges and opportunities for explainable AI (XAI) in molecular design and evaluate the benefits of incorporating domain-specific knowledge into XAI approaches for model refinement, experimental design, and hypothesis testing. In this context, we also discuss the current limitations in evaluating results from chemical language models ...

Inverse Molecular Design for the Discovery of Organic Energy Transfer Photocatalysts: Bridging Global and Local Chemical Space Exploration https://doi.org/10.1021/jacs.5c20087 The discovery of new organic photocatalysts (PCs) for energy transfer (EnT) catalysis remains a significant challenge, largely due to the vast and underexplored chemical space and the delicate balance of the photocatalytic properties. While transition-metal catalysts are effective, their high cost and environmental impact ...

Digitized dataset of aqueous dissociation constants🔥 https://chemrxiv.org/doi/full/10.26434/chemrxiv-2026-6khcw In this work, we release the IUPAC Digitized pKa Dataset, a digital version of a critically-assessed collection of data compiled up to 1970. The dataset includes metadata such as temperature, measurement method, assessed reliability of data, and chemical identifiers such as SMILES and InChI strings. The dataset spans 24,222 entries across 10,564 unique molecules, making it the largest...

Самое интересное, что можно подключить свою кастомную базу или датасет. Первым делом попробовал подключить BigSolDB v2.1. Естественно сам код MCP за меня написал Claude :) Пару минут деплоя и все работает. После этого Claude видит все инструменты и умеет…

Collective intelligence for AI-assisted chemical synthesis https://www.nature.com/articles/s41586-026-10131-4 Here we introduce MOSAIC (Multiple Optimized Specialists for AI-assisted Chemical Prediction), a computational framework that enables chemists to harness the collective knowledge of millions of reaction protocols. MOSAIC is built upon the Llama-3.1-8B-instruct architecture, training 2,498 specialized chemical experts within Voronoi-clustered spaces. This approach delivers reproducible an...