This work bridges the gap between data-driven and physics-based approaches, providing a scalable solution for pesticide discovery when target-specific data are limited, and incorporating meta-learning and MD insights improves cross-species transferability and yields interpretable attention patterns based on biophysical principles.
A multimodal BCL-2 repurposing workflow built with diffusion-based generative modeling for ligand-specific complex generation and an extended neural relational inference (NRI) framework for trajectory-level interaction analysis is presented, establishing a strategy that couples generative complex prediction with graph-...
Ehsan Sayyah, H. Tunc, A. Çelebi et al.· Journal of Chemical Informat...· 0 citations
FerrGAT, a graph attention network framework that addresses molecular bioactivity in low-data regimes through domain-relevant multi-task pre-training and dual-channel molecular representation learning, provides an effective and interpretable framework for molecular property prediction in data-limited scenarios.
The results demonstrate the effectiveness of integrating multi-scale and multi-modal representations with cross-scale alignment for protein–RNA affinity prediction, and suggest that M2-PRNet can highlight relevant RNA-binding regions and support preliminary discrimination between strong and weak binders when plausible...
Junkai Wang, G. Luo, Yun-Song Yang et al.· Bioinformatics· 0 citations
Comprehensive evaluations indicate that GAMT-GINE can effectively utilize continuous spatial information and heterogeneous affinity labels, achieving good predictive accuracy and cross-dataset generalization capability.
Jiarui Li, Hongquan Li, Di Wu et al.· International Journal of Mol...· 0 citations
An open, control-validated computational pipeline for natural-product target hypothesis generation is presented and GPR35 is proposed as a prioritized, experimentally testable target and the workflow is released as a reusable tool.
The potential of MEGA-CL as a generalizable framework for accelerating in silico ADMET evaluation and early-stage drug candidate optimization is demonstrated, with robust performance on challenging regression tasks, including clearance (CL) and steady-state volume of distribution (VDss).
Ting-Hui Jin, Ke-Du Jin, Ying Li et al.· arXiv.org· 0 citations
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