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New generative model InVirtuoGen advances fragment-based drug discovery

By PulseAugur Editorial · [1 sources] ·

Researchers have developed InVirtuoGen, a novel discrete flow generative model designed for fragment-based drug discovery. This model shifts the generation paradigm from completion to refinement, allowing for more effective de novo molecule generation and property optimization. InVirtuoGen has demonstrated superior performance on the Practical Molecular Optimization benchmark and achieved higher docking scores in lead optimization compared to existing methods. The project emphasizes open science by releasing pretrained checkpoints and code for reproducibility. AI

IMPACT Establishes a new state-of-the-art for molecular optimization, potentially accelerating drug discovery pipelines.

RANK_REASON This is a research paper describing a new model and benchmark results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New generative model InVirtuoGen advances fragment-based drug discovery

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This is a research paper describing a new model and benchmark results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Benno Kaech, Luis Wyss, Karsten Borgwardt, Gianvito Grasso ·

    Refine Drugs, Don't Complete Them: Uniform-Source Discrete Flows for Fragment-Based Drug Discovery

    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 model learns to transform a uniform …

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