ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
A Multi-Task Self-Supervised Strategy for Predicting Molecular Properties and FGFR1 Inhibitors.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- The Applications of Machine Learning in Micro-Nano Materials Research: From High-Throughput Screening to Intelligent Design.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- A Unified Hierarchical Multiscale Fusion Framework for Drug-Target Affinity Prediction: From Benchmark Performance to Nanomolar Inhibitor Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A benchmark dataset and interpretable deep learning framework for drug-induced developmental neurotoxicity prediction.RSC advances · 2026Article
- MacAma: Multi-AI Agent as a Co-Scientist for Automated Meta-Analysis.Smart medicine · 2026Article
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- Integration of Transcriptomics With Interpretable Artificial Intelligence for Identifying Molecular Signatures of Physiological Stress in Sleep Deprivation.Journal of cellular and molecular medicine · 2026Article
- STGAT: spatial domain identification of consecutive slices based on graph contrastive learning.Briefings in bioinformatics · 2026Article
- Single-Cell Transcriptomic Analysis of Chemotherapy-Induced Changes in Osteosarcoma With a Pyroptosis-Related Gene-Based Prognostic Model.Journal of cellular and molecular medicine · 2026Article
- KCNK9 could be a prognostic biomarker in uterine corpus endometrial carcinoma.Translational cancer research · 2026Article
- Spectral Decomposition of Chemical Semantics for Activity Cliffs-Aware Molecular Property Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- ARID3A Dysregulation Drives Colon Cancer Progression and Enhances Responsiveness to Aspirin.Journal of cellular and molecular medicine · 2026Article
- ADFC-ATP: Attention-Guided Dual-View Fusion and Contrastive Pretraining for Robust Aquatic Toxicity Prediction.Journal of cellular and molecular medicine · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- Internet-delivered cognitive behavioral therapy for insomnia: The future of insomnia treatment with large language models.Sleep medicine: X · 2025Review
- Long Noncoding RNA function prediction via multiview cross-contrastive learning combined with multiscale semantic adaptive optimization.Briefings in bioinformatics · 2025Article
- MVSGDR: multi-view stacked graph convolutional network for drug repositioning.Briefings in bioinformatics · 2025Article
- eCBT-I dialogue system: a comparative evaluation of large language models and adaptation strategies for insomnia treatment.Journal of translational medicine · 2025Article
- QKDTI A quantum kernel based machine learning model for drug target interaction prediction.Scientific reports · 2025Article
- scRECL: representative ensembles with contrastive learning for scRNA-seq data clustering analysis.Briefings in bioinformatics · 2025Article
- A Multi-Task Self-Supervised Strategy for Predicting Molecular Properties and FGFR1 Inhibitors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Studying the molecular properties of drugs and their interactions with human targets aids in better understanding the clinical performance of drugs and guides drug development. In computer-aided drug discovery, it is crucial to utilize effective molecular feature representations for predicting molecular properties and designing ligands with high binding affinity to targets. However, designing an effective multi-task and self-supervised strategy remains a significant challenge for the pretraining framework. In this study, a multi-task self-supervised deep learning framework is proposed, MTSSMol, which utilizes ≈10 million unlabeled drug-like molecules for pretraining to identify potential inhibitors of fibroblast growth factor receptor 1 (FGFR1). During the pretraining of MTSSMol, molecular representations are learned through a graph neural networks (GNNs) encoder. A multi-task self-supervised pretraining strategy is proposed to fully capture the structural and chemical knowledge of molecules. Extensive computational tests on 27 datasets demonstrate that MTSSMol exhibits exceptional performance in predicting molecular properties across different domains. Moreover, MTSSMol's capability is validated to identify potential inhibitors of FGFR1 through molecular docking using RoseTTAFold All-Atom (RFAA) and molecular dynamics simulations. Overall, MTSSMol provides an effective algorithmic framework for enhancing molecular representation learning and identifying potential drug candidates, offering a valuable tool to accelerate drug discovery processes. All of the codes are freely available online at https:// github.com/zhaoqi106/MTSSMol.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.