ReviewThe AAPS journal2018
Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era.
Review in The AAPS journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 86 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
86 citing papers in PubMed.
- Adaptable Covalent Organic Framework-Hydrogel Microneedles for Glucose-Responsive Isoliquiritigenin Delivery in Diabetic Wound Therapy.Advanced healthcare materials · 2026Article
- Machine learning-guided discovery and molecular dynamics simulation of VISTA-targeted immunotherapeutics for cancer immunotherapy.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- The Evolution of Machine Learning in Medicinal Chemistry: A Comprehensive Bibliometric Analysis.Current neuropharmacology · 2026Review
- Artificial intelligence-assisted neuropharmacology: reshaping drug discovery and translational strategies for neurological diseases.Turkish journal of medical sciences · 2026Review
- Review
- GCN-BBB: Deep Learning Blood-Brain Barrier (BBB) Permeability PharmacoAnalytics with Graph Convolutional Neural (GCN) Network.The AAPS journal · 2025Article
- Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools.Biomarker research · 2025Review
- Advancing Pharmaceutical Science with Artificial Neural Networks: A Review on Optimizing Drug Delivery Systems Formulation.Current pharmaceutical design · 2025Review
- In-silico-based lead optimization of hit compounds targeting mitotic kinesin Eg5 for cancer management.In silico pharmacology · 2025Article
- Using AI in Optimizing Oral and Dental Diagnoses-A Narrative Review.Diagnostics (Basel, Switzerland) · 2024Review
- The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges.Molecular therapy. Nucleic acids · 2024Review
- Application progress of deep generative models in de novo drug design.Molecular diversity · 2024Review
- High-throughput structure determination of an intrinsically disordered protein using cell-free protein crystallization.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Identifying Substructures That Facilitate Compounds to Penetrate the Blood-Brain Barrier via Passive Transport Using Machine Learning Explainer Models.ACS chemical neuroscience · 2024Article
- A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation.Briefings in bioinformatics · 2024Article
- Article
- Modulating autophagy to treat diseases: A revisited review on in silico methods.Journal of advanced research · 2024Review
- A new paradigm for applying deep learning to protein-ligand interaction prediction.Briefings in bioinformatics · 2024Article
- Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer.Current drug delivery · 2024Review
- Machine Learning Research Trends in Traditional Chinese Medicine: A Bibliometric Review.International journal of general medicine · 2024Review
26 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issuedCorrection to: Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era.2018 · Xie XS [corrected to Xie XQ]
Authors and funding
5 authors.
Funding
Abstract
Over the last decade, deep learning (DL) methods have been extremely successful and widely used to develop artificial intelligence (AI) in almost every domain, especially after it achieved its proud record on computational Go. Compared to traditional machine learning (ML) algorithms, DL methods still have a long way to go to achieve recognition in small molecular drug discovery and development. And there is still lots of work to do for the popularization and application of DL for research purpose, e.g., for small molecule drug research and development. In this review, we mainly discussed several most powerful and mainstream architectures, including the convolutional neural network (CNN), recurrent neural network (RNN), and deep auto-encoder networks (DAENs), for supervised learning and nonsupervised learning; summarized most of the representative applications in small molecule drug design; and briefly introduced how DL methods were used in those applications. The discussion for the pros and cons of DL methods as well as the main challenges we need to tackle were also emphasized.
Indexed as
Identifiers
What OpenQuestion holds
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.