ReviewMethods in molecular biology (Clifton, N.J.)2022
Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges.
Review in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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.
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Who cites it
11 citing papers in PubMed.
- Drug Design and Delivery for Intracellular Bacteria: Emerging Paradigms.Drug development research · 2025Review
- Scaffold Hopping with Generative Reinforcement Learning.Journal of chemical information and modeling · 2025Article
- Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.Chemical research in toxicology · 2025Review
- Graph neural processes for molecules: an evaluation on docking scores and strategies to improve generalization.Journal of cheminformatics · 2024Article
- ACEGEN: Reinforcement Learning of Generative Chemical Agents for Drug Discovery.Journal of chemical information and modeling · 2024Article
- MolScore: a scoring, evaluation and benchmarking framework for generative models in de novo drug design.Journal of cheminformatics · 2024Article
- Exploring the current and prospective role of artificial intelligence in disease diagnosis.Annals of medicine and surgery (2012) · 2024Review
- On the difficulty of validating molecular generative models realistically: a case study on public and proprietary data.Journal of cheminformatics · 2023Article
- DataPype: A Fully Automated Unified Software Platform for Computer-Aided Drug Design.ACS omega · 2023Article
- Augmented Hill-Climb increases reinforcement learning efficiency for language-based de novo molecule generation.Journal of cheminformatics · 2022Article
- Recurrent neural network (RNN) model accelerates the development of antibacterial metronidazole derivatives.RSC advances · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Artificial intelligence (AI) has undergone rapid development in recent years and has been successfully applied to real-world problems such as drug design. In this chapter, we review recent applications of AI to problems in drug design including virtual screening, computer-aided synthesis planning, and de novo molecule generation, with a focus on the limitations of the application of AI therein and opportunities for improvement. Furthermore, we discuss the broader challenges imposed by AI in translating theoretical practice to real-world drug design; including quantifying prediction uncertainty and explaining model behavior.
Indexed as
Identifiers
34731463What 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.