ReviewQuantitative biology (Beijing, China)2023
Advanced deep learning methods for molecular property prediction.
Review in Quantitative biology (Beijing, China), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Machine-Learning-Driven Molecular Design and Structure-Property-Performance Relationships in Pharmaceutical Chemistry.Molecules (Basel, Switzerland) · 2026Review
- CataCon: a contrastive graph representation learning framework for catalyst prediction.Journal of cheminformatics · 2026Article
- A pipeline for developing AI-driven models to predict molecular initiating events: a case study on neural tube defects.Journal of cheminformatics · 2026Article
- Beyond Tanimoto: a learned bioactivity similarity index enhances ligand discovery.Frontiers in bioinformatics · 2025Article
- Advanced deep learning methods for molecular property prediction.Quantitative biology (Beijing, China) · 2023Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The prediction of molecular properties is a crucial task in the field of drug discovery. Computational methods that can accurately predict molecular properties can significantly accelerate the drug discovery process and reduce the cost of drug discovery. In recent years, iterative updates in computing hardware and the rise of deep learning have created a new and effective path for molecular property prediction. Deep learning methods can leverage the vast amount of data accumulated over the years in drug discovery and do not require complex feature engineering. In this review, we summarize molecular representations and commonly used datasets in molecular property prediction models and present advanced deep learning methods for molecular property prediction, including state-of-the-art deep learning networks such as graph neural networks and Transformer-based models, as well as state-of-the-art deep learning strategies such as 3D pre-train, contrastive learning, multi-task learning, transfer learning, and meta-learning. We also point out some critical issues such as lack of datasets, low information utilization, and lack of specificity for diseases.
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Registered trials
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.