ReviewBriefings in bioinformatics2022
Representation of molecules for drug response prediction.
Review in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- Toward an AI Era: Application of Artificial Intelligence in Inclusion Complex Screening.Pharmaceutics · 2026Review
- Foundation models and deep learning for cancer drug response prediction: a framework for data, metrics, and validation.Briefings in bioinformatics · 2026Review
- AHR/NRF2 Dual Agonist Prediction and Natural Compound Screening Based on Machine Learning: A New Strategy for the Treatment of Atopic Dermatitis.International journal of molecular sciences · 2026Article
- Hepatotoxicity prediction for traditional Chinese medicine: a two-step in silico framework integrating network and machine learning approaches.BMC complementary medicine and therapies · 2026Article
- Genomic innovations in cancer prevention, diagnosis, prognosis and precision therapeutics.Frontiers in genetics · 2026Review
- Article
- Rethinking network analysis in ethnopharmacology: a multi-omics and AI roadmap to overcome conceptual and methodological biases.Frontiers in pharmacology · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- Representation of Molecules by Sequences of Instructions.Journal of chemical information and modeling · 2025Article
- DTLCDR: A target-based multimodal fusion deep learning framework for cancer drug response prediction.Journal of pharmaceutical analysis · 2025Article
- Article
- Generative Artificial Intelligence in the Metaverse Era: A Review on Models and Applications.Research (Washington, D.C.) · 2025Review
- Machine learning-based pan-cancer study of classification and mechanism of BRAF inhibitor resistance.Translational cancer research · 2024Article
- StructNet-DDI: Molecular Structure Characterization-Based ResNet for Prediction of Drug-Drug Interactions.Molecules (Basel, Switzerland) · 2024Article
- TransCDR: a deep learning model for enhancing the generalizability of drug activity prediction through transfer learning and multimodal data fusion.BMC biology · 2024Article
- Advances in AI and machine learning for predictive medicine.Journal of human genetics · 2024Review
- MolLM: a unified language model for integrating biomedical text with 2D and 3D molecular representations.Bioinformatics (Oxford, England) · 2024Article
- A comprehensive review of artificial intelligence for pharmacology research.Frontiers in genetics · 2024Review
- MOViDA: multiomics visible drug activity prediction with a biologically informed neural network model.Bioinformatics (Oxford, England) · 2023Article
- SynAI: an AI-driven cancer drugs synergism prediction platform.Bioinformatics advances · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The rapid development of machine learning and deep learning algorithms in the recent decade has spurred an outburst of their applications in many research fields. In the chemistry domain, machine learning has been widely used to aid in drug screening, drug toxicity prediction, quantitative structure-activity relationship prediction, anti-cancer synergy score prediction, etc. This review is dedicated to the application of machine learning in drug response prediction. Specifically, we focus on molecular representations, which is a crucial element to the success of drug response prediction and other chemistry-related prediction tasks. We introduce three types of commonly used molecular representation methods, together with their implementation and application examples. This review will serve as a brief introduction of the broad field of molecular representations.
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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.