ArticleMethods in molecular biology (Clifton, N.J.)2022
Has Artificial Intelligence Impacted Drug Discovery?
Article 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 10 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
10 citing papers in PubMed.
- From Executor to Orchestrator: The Pharmacology Scientist in the Age of Agentic AI.Clinical pharmacology and therapeutics · 2026Review
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- The Six Ds of Exponentials and drug discovery: A path toward reversing Eroom's law.Drug discovery today · 2025Review
- An Automated Purification Workflow Coupled with Material-Sparing High-ThroughputACS medicinal chemistry letters · 2024Article
- Sample efficient reinforcement learning with active learning for molecular design.Chemical science · 2024Article
- Accelerating COVID-19 Drug Discovery with High-Performance Computing.Methods in molecular biology (Clifton, N.J.) · 2024Review
- Prediction of Compound Plasma Concentration-Time Profiles in Mice Using Random Forest.Molecular pharmaceutics · 2023Article
- Transformer Performance for Chemical Reactions: Analysis of Different Predictive and Evaluation Scenarios.Journal of chemical information and modeling · 2023Article
- Human-in-the-loop assisted de novo molecular design.Journal of cheminformatics · 2022Article
- MegaSyn: Integrating Generative Molecular Design, Automated Analog Designer, and Synthetic Viability Prediction.ACS omega · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) tools find increasing application in drug discovery supporting every stage of the Design-Make-Test-Analyse (DMTA) cycle. The main focus of this chapter is the application in molecular generation with the aid of deep neural networks (DNN). We present a historical overview of the main advances in the field. We analyze the concepts of distribution and goal-directed learning and then highlight some of the recent applications of generative models in drug design with a focus into research work from the biopharmaceutical industry. We present in some more detail REINVENT which is an open-source software developed within our group in AstraZeneca and the main platform for AI molecular design support for a number of medicinal chemistry projects in the company and we also demonstrate some of our work in library design. Finally, we present some of the main challenges in the application of AI in Drug Discovery and different approaches to respond to these challenges which define areas for current and future work.
Indexed as
Identifiers
34731468What 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.