ArticleJournal of integrative bioinformatics2022
Evaluating molecular representations in machine learning models for drug response prediction and interpretability.
Article in Journal of integrative bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Machine learning-enabled chemical ecology for integrated pest management: from volatiles to field applications.Journal of insect science (Online) · 2026Review
- Artificial intelligence in impurity prediction: current landscape, challenges, and future directions.Journal of computer-aided molecular design · 2026Review
- Count your bits: fingerprint benchmarking to assess broad chemical space representation.Journal of cheminformatics · 2026Article
- AGAPE (Computational G‑Quadruplex Stabilization Prediction): The First Machine Learning Workflow for G‑Quadruplex Stabilization Prediction.ACS omega · 2026Article
- Deep Learning for Anticancer Drug Discovery Targeting Non-Apoptotic Regulated Cell Death Mechanisms.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Ensemble learning-guided discovery of anti-tuberculosis phytochemicals: computational prediction and mechanistic insights.Journal of computer-aided molecular design · 2026Article
- TMolNet: a task-aware multimodal neural network for molecular property prediction.Molecular diversity · 2026Article
- Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery.Advanced drug delivery reviews · 2026Review
- Structure-Activity Relationships and Design of Focused Libraries Tailored for Staphylococcus Aureus Inhibition.Molecular informatics · 2025Article
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- Representation of Molecules by Sequences of Instructions.Journal of chemical information and modeling · 2025Article
- The topology of molecular representations and its influence on machine learning performance.Journal of cheminformatics · 2025Article
- Prediction of blood-brain barrier and Caco-2 permeability through the Enalos Cloud Platform: combining contrastive learning and atom-attention message passing neural networks.Journal of cheminformatics · 2025Article
- The physics-AI dialogue in drug design.RSC medicinal chemistry · 2025Review
- Applications of Machine Learning Approaches for the Discovery of SARS-CoV-2 PLpro Inhibitors.Journal of chemical information and modeling · 2025Article
- DMAGCL: A dual-masked adaptive graph contrastive learning framework for predicting circRNA-drug sensitivity.Frontiers in genetics · 2025Article
- Deepmol: an automated machine and deep learning framework for computational chemistry.Journal of cheminformatics · 2024Article
- Interpretable molecular encodings and representations for machine learning tasks.Computational and structural biotechnology journal · 2024Article
- PANCDR: precise medicine prediction using an adversarial network for cancer drug response.Briefings in bioinformatics · 2024Article
- Exploiting Pharma 4.0 Technologies in the Non-Biological Complex Drugs Manufacturing: Innovations and Implications.Pharmaceutics · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) is increasingly being used to guide drug discovery processes. When applying ML approaches to chemical datasets, molecular descriptors and fingerprints are typically used to represent compounds as numerical vectors. However, in recent years, end-to-end deep learning (DL) methods that can learn feature representations directly from line notations or molecular graphs have been proposed as alternatives to using precomputed features. This study set out to investigate which compound representation methods are the most suitable for drug sensitivity prediction in cancer cell lines. Twelve different representations were benchmarked on 5 compound screening datasets, using DeepMol, a new chemoinformatics package developed by our research group, to perform these analyses. The results of this study show that the predictive performance of end-to-end DL models is comparable to, and at times surpasses, that of models trained on molecular fingerprints, even when less training data is available. This study also found that combining several compound representation methods into an ensemble can improve performance. Finally, we show that a
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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.