ArticleJournal of chemical information and modeling2024
Can Pretrained Models Really Learn Better Molecular Representations for AI-Aided Drug Discovery?
Article in Journal of chemical information and modeling, 2024. 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.
- ACES-GNN: can graph neural network learn to explain activity cliffs?Digital discovery · 2025Article
- The future of pharmaceuticals: Artificial intelligence in drug discovery and development.Journal of pharmaceutical analysis · 2025Review
- Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation.Nature communications · 2025Article
- PTB-DDI: An Accurate and Simple Framework for Drug-Drug Interaction Prediction Based on Pre-Trained Tokenizer and BiLSTM Model.International journal of molecular sciences · 2024Article
- Bidirectional generation of structure and properties through a single molecular foundation model.Nature communications · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Self-supervised pretrained models are gaining increasingly more popularity in AI-aided drug discovery, leading to more and more pretrained models with the promise that they can extract better feature representations for molecules. Yet, the quality of learned representations has not been fully explored. In this work, inspired by the two phenomena of Activity Cliffs (ACs) and Scaffold Hopping (SH) in traditional Quantitative Structure-Activity Relationship analysis, we propose a method named
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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.