QALPA: Property-guided diffusion modeling for efficient exploration of chemical spaces of flexible molecules
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
The Flow has not summarised this story yet — read it at arXiv AI.
Exploring the chemical space of flexible molecules remains challenging because the vast number of possible compounds and conformations, together with the increasing cost and limited generalization of...
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The paper presents a closed‑loop molecule generation pipeline that iteratively retrains on new quantum‑chemical simulation data, overcoming limitations of static generative models. This approach produces molecules whose properties extend up to 0.44 standard deviations beyond the training set and improves out‑of‑distribution classification accuracy by 79%. By conditioning on thermodynamic stability during the loop, the method yields a 3.5‑fold increase in the proportion of stable, potentially synthesizable molecules.
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Mol-JEPA is a scalable multimodal framework that learns molecular world models by using modality masking instead of suboptimal perturbations. It incorporates diverse data such as molecular structures, cellular phenotypes, binding affinities, ADMET profiles, quantum chemistry simulations, and other drug‑discovery information. Benchmarks show that the representations it learns perform strongly, highlighting the benefit of embedding biochemical context via latent‑space prediction.