arXiv Machine Learning
Evolutionary One-Step Generators: Fast and Diverse Sampling for Discrete Design
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arXiv Machine Learning
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Elite-Weighted Supervised Fine-tuning for Goal-Directed Molecular Optimization
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...
arXiv AI
Oct 7
Navigating Route Latent Space for Synthesizable Molecular Design
arXiv:2610.07560v1 Announce Type: new Abstract: Goal-directed molecular design has advanced rapidly, yet a substantial proportion of designed molecules remain difficult to synthesize in practice, lim...
arXiv Machine Learning
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Inference-Time Steering Across Scaffolds and Epitopes for Sample-Efficient De Novo Nanobody Design
The paper introduces dynamic inference-time steering methods for de novo nanobody design, addressing the inefficiency of fixed-condition generative models. Two new budget allocation strategies are proposed: a joint Global Sequential Monte Carlo (SMC) approach that steers and allocates compute across multiple scaffold and epitope configurations simultaneously, and a decoupled Batched Successive Elimination (BaSE) method that adapts resource allocation during inference. Experiments show that these dynamic allocators boost the yield of designable candidates per GPU-hour by nearly fivefold while maintaining diversity across therapeutic targets and generative settings.
arXiv Machine Learning
Aug 4
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.
arXiv Machine Learning
Jul 15
Sample Efficient Generative Optimization for Molecular Design
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.
arXiv Machine Learning
Jul 22
Adopting Reinforcement Learning with Verifiable Rewards for Molecular Generation
arXiv:2607. 19044v1 Announce Type: new Abstract: Leveraging large language models (LLMs) for molecular generation has shown remarkable potential in chemical and drug design.