ReviewMethods in molecular biology (Clifton, N.J.)2022
Deep Learning Applied to Ligand-Based De Novo Drug Design.
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
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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.
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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.
- The architecture of computational antiviralism: a multi-scale framework from molecular targeting to viral ecosystem engineering.Molecular diversity · 2026Review
- Molecular Glue-Design-Evaluator (MOLDE): An Advanced Method for In-Silico Molecular Glue Design.ACS omega · 2025Article
- Pharmacological Chaperones and Protein Conformational Diseases: Approaches of Computational Structural Biology.International journal of molecular sciences · 2023Review
- The Commoditization of AI for Molecule Design.Artificial intelligence in the life sciences · 2022Article
- Reactivity of Covalent Fragments and Their Role in Fragment Based Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2022Review
- Fragment-based drug discovery-the importance of high-quality molecule libraries.Molecular oncology · 2022Review
- Implementation of an AI-assisted fragment-generator in an open-source platform.RSC medicinal chemistry · 2022Article
- Computer-Aided Drug Design Boosts RAS Inhibitor Discovery.Molecules (Basel, Switzerland) · 2022Review
- Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review.Frontiers in bioinformatics · 2022Article
- MegaSyn: Integrating Generative Molecular Design, Automated Analog Designer, and Synthetic Viability Prediction.ACS omega · 2022Article
- On the Value of Using 3D Shape and Electrostatic Similarities in Deep Generative Methods.Journal of chemical information and modeling · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the latest years, the application of deep generative models to suggest virtual compounds is becoming a new and powerful tool in drug discovery projects. The idea behind this review is to offer an updated view on de novo design approaches based on artificial intelligent (AI) algorithms, with a particular focus on ligand-based methods. We start this review by reporting a brief overview of the most relevant de novo design approaches developed before the use of AI techniques. We then describe the nowadays most common neural network architectures employed in ligand-based de novo design, together with an up-to-date list of more than 100 deep generative models found in the literature (2017-2020). In order to show how deep generative approaches are applied into drug discovery context, we report all the now available studies in which generated compounds have been synthetized and their biological activity tested. Finally, we discuss what we envisage as beneficial future directions for further application of deep generative models in de novo drug design.
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Identifiers
34731474What 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.