Improving Molecular Property Prediction in Small Language Models Using Graph-bas

arXiv:2603.18256v1 Announce Type: cross Abstract: Recent advances in reasoning-based large language models (LLMs) have demonstrated substantial improvements in complex

arXiv:2604.27269v1 Announce Type: new Abstract: Biomedical knowledge graphs (KGs) are widely used in the life sciences, yet many are derived from unstructured documents and

arXiv:2609.03635v1 Announce Type: new Abstract: Clinical trials in the UK can cost up to {\pounds}1.3 million, with approximately 90% drug failure rate. Toxicity is a major

arXiv:2607.18332v2 Announce Type: cross Abstract: Accurate prediction of ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) is important for drug discovery.

arXiv:2604.08553v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown strong performance on text-attributed graphs (TAGs) due to their superior semantic

arXiv:2609.13519v1 Announce Type: new Abstract: Molecular design is most effective when generation mirrors the edits chemists actually make: extending a scaffold, replacing a

arXiv:2604.00025v1 Announce Type: cross Abstract: Standard evaluation protocols reveal a counterintuitive phenomenon: on 7.7% of benchmark problems spanning five datasets, larger

arXiv:2608.20755v1 Announce Type: new Abstract: Modern de novo design workflows generate many candidate protein binders, but wet-lab validation capacity remains limited, making