Deep learning enables rapid identification of potent DDR1 kinase inhibitors

By A. Zhavoronkov, Y. Ivanenkov, Alex Aliper, M. Veselov, V. Aladinskiy, Anastasiya V Aladinskaya, V. Terentiev, Daniil Polykovskiy, Maksim Kuznetsov, Arip Asadulaev and 12 more. Nature Biotechnology, 2019

1,252 citations recorded by the source corpus.

Subfields: Molecular Biology, Oncology

View Paper: https://www.semanticscholar.org/paper/d44ac0a7fd4734187bccafc4a2771027b8bb595e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Gisbert Schneider
2. Petra Schneider
3. Shengchao Liu
4. Vsevolod Katritch
5. Anastasiia V. Sadybekov
6. Rohan Gupta
7. Eric J. Topol
8. W. Patrick Walters

In Oncology

1. Gisbert Schneider
2. W. Patrick Walters

Citations in the corpus

• MoFlow: An Invertible Flow Model for Generating Molecular Graphs (2020, background): “Recently, deep graph generative models have demonstrated their big potential to accelerate the drug discovery process by exploring large chemical space in a data-driven manner [12, 35].”
• Differentiable Scaffolding Tree for Molecular Optimization (2021, neutral): “The structural design of new functional molecules, also called molecular optimization, is the key to many scientific and engineering challenges, such as finding energy storage materials [16, 19], small molecule pharmaceutics [29, 51], and environment-friendly material [53].”
• Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics (2019, background): “The structural and activity data of patented agents are useful for such tasks as the study of discovery landscapes and opportunities (34,35), as well as the development of artificial intelligence tools (36).”
• Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine (2024, methodology): “By using an in-house AI algorithm after screening billions of molecules and identifying a promising candidate that moved on into preclinical trials [50,122].”
• Utilizing graph machine learning within drug discovery and development (2021, methodology): “With the design, synthesis and testing of de novo small molecules [48], the in vitro and in vivo testing of drug repurposing hypotheses [50], and target identification frameworks being conceptualized [47], we are potentially entering into a golden age of validation for GML within drug discovery and ”
• Artificial intelligence to deep learning: machine intelligence approach for drug discovery (2021, methodology): “With the help of GEN-TRL (https:// github. com / insil icome dicine/ GENTRL), Zhavoronkov et al. discovered novel inhibitors of an enzyme, DDR1 kinase [118].”
• Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets (2022, neutral): “Copyright 2022 by the author(s). against kinase DDR1 (Zhavoronkov et al., 2019).”
• Rethinking drug design in the artificial intelligence era (2019, methodology): “In a recent example focused on inhibitors of the kinase discoidin domain receptor 1 (DDR1) 149 , deep learning was used to virtually generate a set of molecules and prioritize a small set of compounds for rapid synthesis and testing.”
• Identification of antimicrobial peptides from the human gut microbiome using deep learning (2022, background): “Machine learning has already successfully identified small molecules with antibiotic effects, such as hialicin 19 , as well as inhibitors for DDR1 enzyme from small molecules 20 .”