arXiv Machine Learning
Active Learning Enables Generation of Molecules that Advance the Known Pareto Front
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arXiv AI
3d ago
QALPA: Property-guided diffusion modeling for efficient exploration of chemical spaces of flexible molecules
arXiv:2609.16527v1 Announce Type: cross Abstract: Exploring the chemical space of flexible molecules remains challenging because the vast number of possible compounds and conformations, together with...
arXiv AI
Jun 30
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 AI
2d ago
BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models
BOOM is a new benchmark for evaluating out‑of‑distribution (OOD) molecular property predictions in machine learning. It provides chemically‑informed tests across common property prediction tasks and assesses over 150 model‑task combinations. The study shows that current models, including chemical foundation models, struggle to generalize OOD, with the best model still exhibiting three times higher error than in‑distribution predictions.
arXiv Machine Learning
Jun 10
Synthesizable Molecular Generation via Soft-constrained GFlowNets with Rich Chemical Priors
arXiv:2602. 04119v2 Announce Type: replace Abstract: The application of generative models for experimental drug discovery campaigns is severely limited by the difficulty of designing molecules de novo that can be synthesized in practice.
arXiv Machine Learning
Jul 1
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
Jul 16
Implementations of Quantum and Classical Topology-Aligned Architectures for Molecular Property Prediction
arXiv:2607. 13737v1 Announce Type: new Abstract: For low-data and resource-constrained regimes typical of quantum chemistry, parameter-efficient learning is a key objective.