Author

M. Cretu

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#artificial intelligence Preprint Oct 2026

Zatom-2: Multitask Pretraining on Atomistic Data for Generative Modeling across Domains

Unified atomistic modeling has the potential to accelerate discovery in chemistry, materials science, and biology by bridging data-rich chemical domains and data-scarce biological contexts. However, existing generative approaches to atomistic modeling remain highly specialized to scientific disciplines (chemistry vs. b...

M. Cretu, A. Abrudan, Antonia Panescu et al. · 0 citations

Jul 2026

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models

DBMol substantially improves pocket coverage while maintaining molecular diversity over unconditional generation, and is competitive under held-out metrics despite the absence of reference-ligand supervision, which support the promise of structure prediction models as effective optimization signals for de novo molecula...

Yiming Qin, Kai Yi, M. Cretu et al. · arXiv.org · 0 citations