Valid Property-Enhanced Contrastive Learning for Targeted Optimization & Resampling for Novel Drug Design

• URL: http://arxiv.org/abs/2509.00684v1
• Date: Sun, 31 Aug 2025 03:55:29 GMT
• Title: Valid Property-Enhanced Contrastive Learning for Targeted Optimization & Resampling for Novel Drug Design
• Authors: Amartya Banerjee, Somnath Kar, Anirban Pal, Debabrata Maiti,
• Abstract summary: VECTOR+ is a framework that couples property-guided representation learning with controllable molecule generation.<n>VECTOR+ generates novel, synthetically tractable candidates.<n>VECTOR+ generalizes to kinase inhibitors, producing compounds with stronger docking scores than established drugs.
• Score: 1.4874449172133888
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Efficiently steering generative models toward pharmacologically relevant regions of chemical space remains a major obstacle in molecular drug discovery under low-data regimes. We present VECTOR+: Valid-property-Enhanced Contrastive Learning for Targeted Optimization and Resampling, a framework that couples property-guided representation learning with controllable molecule generation. VECTOR+ applies to both regression and classification tasks and enables interpretable, data-efficient exploration of functional chemical space. We evaluate on two datasets: a curated PD-L1 inhibitor set (296 compounds with experimental $IC_{50}$ values) and a receptor kinase inhibitor set (2,056 molecules by binding mode). Despite limited training data, VECTOR+ generates novel, synthetically tractable candidates. Against PD-L1 (PDB 5J89), 100 of 8,374 generated molecules surpass a docking threshold of $-15.0$ kcal/mol, with the best scoring $-17.6$ kcal/mol compared to the top reference inhibitor ($-15.4$ kcal/mol). The best-performing molecules retain the conserved biphenyl pharmacophore while introducing novel motifs. Molecular dynamics (250 ns) confirm binding stability (ligand RMSD < $2.5$ angstroms). VECTOR+ generalizes to kinase inhibitors, producing compounds with stronger docking scores than established drugs such as brigatinib and sorafenib. Benchmarking against JT-VAE and MolGPT across docking, novelty, uniqueness, and Tanimoto similarity highlights the superior performance of our method. These results position our work as a robust, extensible approach for property-conditioned molecular design in low-data settings, bridging contrastive learning and generative modeling for reproducible, AI-accelerated discovery.

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