arXiv Machine Learning
PairIT: Autoregressive Transformers for Low-Data Molecule Optimization
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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 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.
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Procedural Pretraining for Molecular Property Prediction
The paper proposes a three‑stage training pipeline that begins with procedural pretraining on abstract, procedurally generated data, followed by molecular pretraining on SMILES, and finally downstream fine‑tuning for molecular property prediction. Experiments show that procedural pretraining improves downstream performance—e.g., a 4.8% error reduction on Lipophilicity—especially when labeled data are scarce, and that the benefit peaks at an intermediate procedural training budget. Analysis indicates that transferable knowledge resides mainly in attention layers, while feed‑forward layers may over‑specialize.
arXiv AI
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Fraglingo: Molecular Design via Attachment-Aware Autoregressive Fragment Generation
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arXiv AI
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Fine-Tuning Diffusion Models for Molecular Generation via Reinforcement Learning and Fast Sampling
arXiv:2606. 01220v1 Announce Type: cross Abstract: Generating molecules that simultaneously satisfy drug-like properties and conform to the 3D structure of a target protein is a core challenge in structure-based drug design (SBDD).
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...