arXiv Machine Learning

Benchmarking graph-based models for in-silico toxicity prediction in drug discovery

arXiv Machine Learning

Jul 28

MEGA-CL: A Molecular Foundation Model for Generalizable ADMET Prediction through Graph External Attention and Contrastive Learning

arXiv:2607. 24314v1 Announce Type: new Abstract: Predicting the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of small molecules remains a major challenge in drug discovery.

By Tinghui Jin, Kedu Jin, Ying Li, Guanghui Ren, Jingzhi Xue, Shiyu Zhou, Xiaoli Dai, Li-bin Wei, Xijing Chen, Di Zhao, Jinfeng Liu

benchmarks safety

arXiv AI

Sep 25

SMILESGNN: Interpretable Clinical Toxicity Prediction via SMILES-Graph Cross-Attention Fusion

SMILESGNN is a multimodal architecture that fuses a SMILES Transformer encoder with a GATv2 graph encoder through cross‑attention, enabling interpretable clinical toxicity predictions. The model retains an explicit graph branch, allowing GNNExplainer to identify substructures linked to toxicity. On the ClinTox dataset it achieves an AUC‑ROC of 0.987 and F1 of 0.906 with only 0.4 M parameters, while on Tox21 it attains a mean AUC‑ROC of 0.750, comparable to strong single‑modality baselines.

By Quang Minh Nguyen, Thuy Quynh Nguyen, Duc Minh Le, Ho Nhat Minh Nguyen, Thanh Long Dai Doan, Trong Nghia Nguyen

llms multimodal safety

arXiv Machine Learning

Jun 18

KEPLA: A Knowledge-Enhanced Deep Learning Framework for Accurate Protein-Ligand Binding Affinity Prediction

arXiv:2506. 13196v5 Announce Type: replace Abstract: Accurate prediction of protein-ligand binding affinity is critical for drug discovery.

By Han Liu, Keyan Ding, Peilin Chen, Yinwei Wei, Liqiang Nie, Dapeng Wu, Shiqi Wang

rag benchmarks safety

arXiv AI

Jun 9

Pharmacogenomic Knowledge Graph Augmentation for Graph Neural Network-Based Drug-Drug Interaction Prediction

arXiv:2606. 07698v1 Announce Type: cross Abstract: Graph neural networks (GNNs) applied to drug-drug interaction (DDI) prediction rely exclusively on molecular structure encoded as SMILES-derived graphs.

By Juergen Dietrich

rag multimodal benchmarks safety

arXiv Machine Learning

Jun 10

TRAPS: Therapeutic Response Analysis via Pathway-informed Stratification

arXiv:2606. 09898v1 Announce Type: new Abstract: Cancer treatment planning requires decisions across multiple clinical dimensions at once.

By Sujoy Banik, Sayantan Chakraborty, Boishakhi Das Toma, Zainab Ghafoor, Ushashi Bhattacharjee, Koushik Howlader, Tirtho Roy

benchmarks

arXiv Machine Learning

Jul 3

An Additive MLP-GNN Framework for Characterizing Chemical and Structural Contributions to Aqueous Solubility

arXiv:2607. 02212v1 Announce Type: cross Abstract: Aqueous solubility is a key property in early-stage drug discovery, but most predictive models merge physicochemical descriptors and molecular graph information into a single representation, obscuring whether a prediction is driven by global chemistry, molecular structure, or both.

By Sampreeti Bhattacharya, Arkaprava Roy

rag fine-tuning

arXiv Machine Learning

Sep 1

MolLedger: An Additive Graph Neural Network with Chemically Grounded ADME Attributions

arXiv:2608.30636v1 Announce Type: new Abstract: Optimizing absorption, distribution, metabolism, and excretion (ADME) is an important part of small molecule drug discovery. Many machine learning mode...

By Christina X. Ji

safety

arXiv Machine Learning

Jun 16

A polarity-aware multi-relational model for the signed interaction prediction in biological networks

arXiv:2407. 07357v3 Announce Type: replace Abstract: Predicting signed interactions in biological networks is crucial for understanding drug mechanisms and facilitating drug repurposing.

By Ziye Zhou, Meijie Wang, Lun Yu

arXiv Machine Learning

Jul 9

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions

arXiv:2607. 07611v1 Announce Type: new Abstract: Background: Graph neural networks improve computational prediction of polypharmacy side effects, but standard binary cross-entropy training allocates equal capacity to well-classified and difficult examples, potentially missing clinically significant interactions.

By Faranak Hatami, Mousa Moradi

rag

arXiv AI

Jun 4

Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification?

arXiv:2506. 10912v4 Announce Type: replace Abstract: Toxicity remains a leading cause of early-stage drug development failure.

By Fei Lin, Ziyang Gong, Cong Wang, Tengchao Zhang, Yonglin Tian, Yining Jiang, Ji Dai, Chao Guo, Xiaotong Yu, Xue Yang, Gen Luo, Fei-Yue Wang

llms efficiency multimodal benchmarks safety

arXiv Machine Learning

Aug 13

Large-scale AI-Ready Data for Anti-Cancer Drug Response Modeling

arXiv:2608. 11444v1 Announce Type: cross Abstract: Drug response prediction (DRP) models are an active area of research in pharmacogenomics, with growing potential to accelerate the identification of effective anticancer drugs.

By Vincent Lavelle, Yitan Zhu, Kaitlyn Marlor, Thomas Brettin, Rick Stevens

benchmarks

arXiv Machine Learning

Jun 9

Do Larger Models Really Win in Drug Discovery? A Benchmark Assessment of Model Scaling in AI-Driven Molecular Property and Activity Prediction

arXiv:2604. 26498v3 Announce Type: replace Abstract: The rapid growth of molecular foundation models and large language models (LLMs) has encouraged a scale centred view of AI in drug discovery, in which larger pretrained models are expected to supersede compact cheminformatics models.

By Jinjiang Guo, Sheng Ding

llms benchmarks safety

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