arXiv AI
QALPA: Property-guided diffusion modeling for efficient exploration of chemical spaces of flexible molecules
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Beyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) Benchmark
arXiv:2606. 30170v1 Announce Type: cross Abstract: Generative molecular design is shaped by simple proxy benchmarks for drug-like properties and models pretrained on large pharmaceutical datasets.
arXiv Machine Learning
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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.
arXiv Machine Learning
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Active Learning Enables Generation of Molecules that Advance the Known Pareto Front
arXiv:2501.02059v2 Announce Type: replace Abstract: Although generative models hold promise for discovering molecules with optimized desired properties, they often fail to suggest synthesizable molec...
arXiv AI
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Comprehensive pKa Data Augmentation from Limited Real Data through an Engineered Models-Quantum Framework
arXiv:2606. 17077v1 Announce Type: cross Abstract: Proton dissociation constants (pKa) are critical for functional molecule discovery and molecular modeling.
arXiv AI
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Mol-JEPA: A multimodal Joint Embedding Predictive Architecture for Molecules
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.
arXiv Machine Learning
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Sesame: Structure-Aware Molecular Generation via Spatial Density-Map Conditioning
arXiv:2606. 23856v1 Announce Type: new Abstract: Generative molecular models for drug design are a promising direction with much active research.