OPDiv is a new algorithm that addresses the tradeoff between ranking quality and chemical diversity in virtual screening. It uses integer optimization to select an optimal subset of top‑k compounds that meet a specified diversity threshold, evaluated with fingerprint, shape, and electrostatic metrics. The method demonstrates how to benchmark virtual screening pipelines by comparing their best achievable diverse selections.
arXiv:2510. 24380v2 Announce Type: replace Abstract: Make-on-demand combinatorial synthesis libraries (CSLs) like Enamine REAL have significantly enabled drug discovery efforts.
arXiv:2606. 26657v1 Announce Type: new Abstract: Identifying high-utility candidates from massive discrete spaces under expensive evaluations is a recurring challenge across the sciences, with structure-based drug discovery as a prominent example.
arXiv:2509. 26405v2 Announce Type: replace Abstract: We introduce InVirtuoGen, a discrete flow generative model for fragmented SMILES for de novo and fragment-constrained generation, and target-property/lead optimization of small molecules.
The paper introduces Mol-E, an evolutionary algorithm that leverages large language models trained on molecular data to generate candidate molecules. Mol-E achieves state‑of‑the‑art performance on the Practical Molecular Optimization benchmark, scoring 17.500 in the task‑agnostic regime and 20.551 in the task‑informed regime. It also outperforms baseline methods in multi‑property optimization tasks involving docking against DRD2, MK2, and AChE.
arXiv:2607. 12488v1 Announce Type: new Abstract: Molecular optimization in drug discovery, materials design, and catalysis requires searching vast chemical spaces under tight evaluation budgets, since high-fidelity oracles and experimental measurements are costly.
TopU-LBVS is a new multi‑target benchmark for ligand‑based virtual screening that addresses shortcomings of existing datasets by using hard‑negative decoys and a fixed 1:40 active‑to‑decoy ratio. It covers 93 protein targets across seven classes, provides three evaluation protocols (full, low‑data, and mini), and includes curated ChEMBL‑35 bioactivity data with property‑matched, structurally similar decoys. The benchmark demonstrates that performance drops sharply when moving from random‑decoy to hard‑negative evaluation, and it releases data, splits, code, and baseline implementations for reproducible comparison.
arXiv:2609.00189v1 Announce Type: new Abstract: Goal-directed optimization is essential for steering molecular generators to propose candidates with desired properties. However, it is often implement...
TopU-LBVS is a new multi‑target benchmark for ligand‑based virtual screening that addresses shortcomings of previous datasets by using hard‑negative decoys and a fixed 1:40 active‑to‑decoy ratio. It covers 93 protein targets across seven classes, provides three evaluation protocols (full, low‑data, and mini), and includes curated ChEMBL‑35 bioactivity data with property‑matched, structurally similar decoys to reduce shortcut learning. The benchmark comes with released data, fixed splits, evaluation code, and baseline implementations for reproducible comparison of LBVS and molecular representation methods.
arXiv:2607. 13155v1 Announce Type: new Abstract: Generative molecular models can support early drug discovery by proposing new candidate compounds de novo.
arXiv:2608. 19906v1 Announce Type: new Abstract: Accurately ranking active ligands for a target protein pocket from massive chemical libraries remains a central challenge in virtual screening.
arXiv:2608. 19808v1 Announce Type: new Abstract: Cyclic peptides are emerging as promising molecular scaffolds in drug discovery due to their high binding affinity and structural stability.
arXiv:2606. 00008v1 Announce Type: new Abstract: Multi-objective molecular optimization requires searching vast chemical spaces under conflicting objectives, where early design decisions strongly constrain downstream outcomes.