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2026 RDKit User Group Meeting (in person and online)

Recently several co-folding AI are available, for example AF3, Chai-1, protenix and openfold. This area is hot and growing day by day. Co-Folding AI has potential for taking SBDD approach without determining the target protein-ligand structure. As most of readers know that there are still limitation of using co-folding AI for SBDD but really useful I think. I have an interest in openfold because openfold disclose code and has good open scientific community.

Welcome to the 2026 Chalmers AI Research Center Workshop for Structured Learning. In this workshop we broadly cover topics related to Structured Learning targeting specifically the following questions...

The Novo Nordisk Foundation Center for Protein Design at the University of Copenhagen seeks to appoint two new group leaders in de novo protein design from 1 February 2027 or as soon as possible thereafter. These positions will be at the Assistant or Associate Professor level and associated with the Department of Biology (Faculty of Science) or the Department of Drug Design and Pharmacology (Faculty of Health and Medical Sciences).

Drug discovery is a dauntingly difficult process due to the enormous space of possible molecules and the complexity of biological systems. Traditional in silico drug design methods have focused primarily on optimising chemical properties, often ...

Computer-aided synthesis planning (CASP) automates retrosynthetic analysis, generally by recursively applying one-step retrosynthesis models within multistep search algorithms to simplify a target mol...

Postdoc in Cell Biology – CRISPR Perturbation and High-Content Imaging, Department of Pharmaceutical Biosciences, Uppsala University

Rocío Mercado Oropeza | Assistant Professor at Chalmers University of Technology Abstract: Generative and predictive machine learning models are reshaping how we explore chemical compound space,…

Large language models (LLMs) are known to generate plausible but false information across a wide range of contexts, yet the real-world magnitude and consequences of this hallucination problem remain p...

Surfactants are structurally diverse molecules whose interfacial properties are central to a wide range of industrial and scientific applications, yet their systematic evaluation remains challenging due to the cost of both wet-lab experiments and ...

Discover the world’s best science and medicine | Nature.com

Tools for reliably estimating what artificial intelligence is being used for are still lacking.

YouTube video by Chalmers AI4Science

PLaTITO incorporates protein language model embeddings into generative molecular dynamics, achieving state-of-the-art equilibrium sampling on out-of-distribution proteins including fast-folding protei...

About the Event Join us for the 2026 symposium on Machine Learning for Materials and Molecular Discoveries (ML2MD). At this symposium, we seek to highlight some of the key research developments at the...

Large language models (LLMs) have seen a widespread adoption in all spheres of science including chemistry and cheminformatics. Nevertheless, our knowledge of how they operate is limited, giving rise to exploration of their capabilities in different areas ...

Surfactants are widely used for industrial applications, yet more environmentally friendly surfactants with enhanced properties are demanded. A key thermodynamic property governing the behavior of a surfactant in an aqueous solution is its critical micelle concentration (CMC). Below the CMC, increasing the surfactant concentration reduces the surface tension of the solution; above the CMC, the water–air interface becomes saturated with adsorbed surfactant, leading excess surfactant to self-assemble into micelles and the surface tension to plateau. Many physicochemical properties of a surfactant solution exhibit sharp changes at the CMC. The conventional experimental protocol to determine the CMC of a surfactant is labor-intensive and time-consuming: (1) prepare many surfactant solutions spanning a wide concentration range and then (2) measure the surface tension of each solution. Herein, we adopt Bayesian experimental design (BED) to determine the CMC of a surfactant more efficiently─even without prior knowledge of its order of magnitude. BED follows an experiment-model-design feedback loop: (1) prepare a surfactant solution and measure its surface tension; (2) use all surface tension data thus far to obtain a posterior distribution over thermodynamic models of the surface tension isotherm of the surfactant; and (3) pick the surfactant concentration for the next experiment to maximize expected information gain about the CMC. We show that BED efficiently gathers information about the CMC using two surfactants (octyl-β-d-thioglucopyranoside and Triton X-100) as test cases. Broadly, BED can reduce the time, effort, cost, and chemical waste to determine the CMC of surfactants and drive an autonomous laboratory for surfactant discovery and characterization.