ArticleJournal of cheminformatics2020
Transformer-CNN: Swiss knife for QSAR modeling and interpretation.
Article in Journal of cheminformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 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.
The trial behind it
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Who cites it
83 citing papers in PubMed, 258 citations in OpenAlex.
- Interpretable Machine Learning Frameworks for QSAR Modeling: A Comparative Study of Classical Algorithms and Transformer-CNN Architectures.ACS omega · 2026Article
- QSAR modeling and in vitro studies of the selective action of triphenylphosphonium salts as anticancer agents against the rhabdomyosarcoma cell line.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Article
- Transformers for molecular property prediction: domain adaptation efficiently improves performance.Journal of cheminformatics · 2026Article
- LG-Transformer: learned-graph transformer framework enabling diverse physicochemical properties prediction toward fuel design.Nature communications · 2026Article
- New Docking, Molecular Dynamics, and QSAR Models to Predict Disruption of Human and Rat Transthyretin Function by Per- and Polyfluoroalkyl Substances (PFAS).Chemical research in toxicology · 2026Article
- SangsterLogP - the largest publicly available dataset of logP values.Scientific data · 2026Article
- Antibacterial Activity and Cytotoxicity Evaluation of 5-Amino-4-cyano-1,3-oxazole Derivatives: Synthesis, In Silico, and In Vitro Studies.Chemistry & biodiversity · 2026Article
- Artificial intelligence in drug discovery from advanced molecular representation to pipeline applications.Frontiers in bioinformatics · 2026Review
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- KRASAVA-An Expert System for Virtual Screening of KRAS G12D Inhibitors.International journal of molecular sciences · 2025Article
- Rethinking the AI Paradigm for Solubility Prediction of Drug‑Like Compounds with Dual-Perspective Modeling and Experimental Validation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- AdapTor: Adaptive Topological Regression for quantitative structure-activity relationship modeling.Journal of cheminformatics · 2025Article
- Curated CYP450 Interaction Dataset: Covering the Majority of Phase I Drug Metabolism.Scientific data · 2025Article
- Advanced machine learning for innovative drug discovery.Journal of cheminformatics · 2025Article
- HDAC3_VS_assistant: cheminformatics-driven discovery of histone deacetylase 3 inhibitors.Molecular diversity · 2025Article
- Consensus Modeling Strategies for Predicting Transthyretin Binding Affinity from Tox24 Challenge Data.Chemical research in toxicology · 2025Article
- Enhancing Transthyretin Binding Affinity Prediction with a Consensus Model: Insights from the Tox24 Challenge.Chemical research in toxicology · 2025Article
- Generalizable, fast, and accurate DeepQSPR with fastprop.Journal of cheminformatics · 2025Article
- Recent Advances in the Modeling of Ionic Liquids Using Artificial Neural Networks.Journal of chemical information and modeling · 2025Review
- Publishing neural networks in drug discovery might compromise training data privacy.Journal of cheminformatics · 2025Article
23 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
Abstract
We present SMILES-embeddings derived from the internal encoder state of a Transformer [1] model trained to canonize SMILES as a Seq2Seq problem. Using a CharNN [2] architecture upon the embeddings results in higher quality interpretable QSAR/QSPR models on diverse benchmark datasets including regression and classification tasks. The proposed Transformer-CNN method uses SMILES augmentation for training and inference, and thus the prognosis is based on an internal consensus. That both the augmentation and transfer learning are based on embeddings allows the method to provide good results for small datasets. We discuss the reasons for such effectiveness and draft future directions for the development of the method. The source code and the embeddings needed to train a QSAR model are available on https://github.com/bigchem/transformer-cnn. The repository also has a standalone program for QSAR prognosis which calculates individual atoms contributions, thus interpreting the model's result. OCHEM [3] environment (https://ochem.eu) hosts the on-line implementation of the method proposed.
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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.