The Michaelis constant (KM) is a key parameter in enzymology. Its experimental measurement is often low-throughput and costly, but machine learning can identify patterns to make predictions in a high-...

Because of the observation that kinetic rates can be better correlated with in vivo drug efficacy than simple affinities, significant efforts have bee…

While experiments and computer simulations to study biological phenomena are usually performed in diluted in vitro conditions, such phenomena happen inside the cellular cytoplasm, an environment dense...

Release_2026.03.1 (Changes relative to Release_2025.09.1) Acknowledgements (Note: I'm no longer attempting to manually curate names. If you would like to see your contribution acknowledged with you...

The recently released Boltz-2 cofolding model is generating high expectations by enabling both protein–ligand structure and binding affinity predictions. When applied to a recently described and chall...

With the upcoming ERC Work Programme 2026 this July, several changes to the submission of applications and the evaluation of proposals will apply. The main novelties for 2026 and 2027 are are summaris...

Google Summer of Code 2026 applications are now open

ALT: a large screen shows a man 's face and says ingsoc

Due to the observation that kinetic rates can be better correlated with in vivo drug efficacy than simple affinities, significant efforts have been made to develop experimental and computational methods to predict drug-target residence times over the last ...

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In recent years, it has become apparent that calculating binding kinetic rates and understanding the binding mechanism of drugs to their target proteins can be helpful in drug design [1–3]. For instan...

The molecular glues modulate the pattern and stability of water-mediated hydrogen bonds between the proteins of ternary complexes.

Annual conference on Free Energy Methods in Drug Discovery

Workshop on generative Models, LLMs, and the Future of Molecular AI. For details, see: https://moleculediscovery.github.io/workshop2025/

Selecting suitable solvents to control the morphology and properties of novel functional materials remains a significant challenge, especially when there is limited or no prior knowledge of the material and its solubility. In this work, we present a solvent selection toolkit for functional porous organic polymers. We have developed the MLoc algorithm for the fast determination of Hansen solubility parameters (HSPs) for novel materials. This approach requires ultraviolet and visible (UV/vis) absorbance data, measured for a number of candidate solvents using a standard laboratory setup. Based on these measurements, MLoc determines the HSPs for novel porous organic materials using a centroid-location algorithm based on Hansen distance. The results of this algorithm can guide the fine-tuning of both morphology and carbon-capture performance of target polymers, which we illustrate in a case study. In this example, performing the polymer synthesis in solvents with HSPs most similar to the porous material has led to CO2 uptake improved by 220% compared to a reported analogue (from 2.16 to 6.95 wt %). Using MLoc, we have also compiled a HSP database for 17 porous organic polymers, enhanced with data for over 80 reactions, sampling different conditions, which we present as a resource for future data-driven research in this area.

In this work, we introduce TorchANI 2.0, a significantly improved version of the free and open source TorchANI software package for training and evaluation of ANI (ANAKIN-ME) deep learning models. Tor...

Discover the final programme of ChemBioChem and ChemPhysChem 25th Anniversary Symposium on Advances in Structural Analysis of Biomolecules! Join us for 1,5 days of exciting science showcasing:  >> 14...