Literature DB >> 33118816

REINVENT 2.0: An AI Tool for De Novo Drug Design.

Thomas Blaschke1, Josep Arús-Pous1,2, Hongming Chen3, Christian Margreitter1, Christian Tyrchan4, Ola Engkvist1, Kostas Papadopoulos1, Atanas Patronov1.   

Abstract

In the past few years, we have witnessed a renaissance of the field of molecular de novo drug design. The advancements in deep learning and artificial intelligence (AI) have triggered an avalanche of ideas on how to translate such techniques to a variety of domains including the field of drug design. A range of architectures have been devised to find the optimal way of generating chemical compounds by using either graph- or string (SMILES)-based representations. With this application note, we aim to offer the community a production-ready tool for de novo design, called REINVENT. It can be effectively applied on drug discovery projects that are striving to resolve either exploration or exploitation problems while navigating the chemical space. It can facilitate the idea generation process by bringing to the researcher's attention the most promising compounds. REINVENT's code is publicly available at https://github.com/MolecularAI/Reinvent.

Year:  2020        PMID: 33118816     DOI: 10.1021/acs.jcim.0c00915

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  18 in total

Review 1.  Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.

Authors:  Vertika Gautam; Anand Gaurav; Neeraj Masand; Vannajan Sanghiran Lee; Vaishali M Patil
Journal:  Mol Divers       Date:  2022-07-11       Impact factor: 3.364

2.  Dual Use of Artificial Intelligence-powered Drug Discovery.

Authors:  Fabio Urbina; Filippa Lentzos; Cédric Invernizzi; Sean Ekins
Journal:  Nat Mach Intell       Date:  2022-03-07

Review 3.  Retro Drug Design: From Target Properties to Molecular Structures.

Authors:  Yuhong Wang; Sam Michael; Shyh-Ming Yang; Ruili Huang; Kennie Cruz-Gutierrez; Yaqing Zhang; Jinghua Zhao; Menghang Xia; Paul Shinn; Hongmao Sun
Journal:  J Chem Inf Model       Date:  2022-06-02       Impact factor: 6.162

4.  Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision, and Reproducibility.

Authors:  Alexander D Wade; Agastya P Bhati; Shunzhou Wan; Peter V Coveney
Journal:  J Chem Theory Comput       Date:  2022-05-24       Impact factor: 6.578

5.  Quantitative Estimate Index for Early-Stage Screening of Compounds Targeting Protein-Protein Interactions.

Authors:  Takatsugu Kosugi; Masahito Ohue
Journal:  Int J Mol Sci       Date:  2021-10-10       Impact factor: 5.923

6.  Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems.

Authors:  John A Keith; Valentin Vassilev-Galindo; Bingqing Cheng; Stefan Chmiela; Michael Gastegger; Klaus-Robert Müller; Alexandre Tkatchenko
Journal:  Chem Rev       Date:  2021-07-07       Impact factor: 60.622

7.  Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery.

Authors:  Manish Kumar Tripathi; Abhigyan Nath; Tej P Singh; A S Ethayathulla; Punit Kaur
Journal:  Mol Divers       Date:  2021-06-23       Impact factor: 3.364

8.  Nonadditivity in public and inhouse data: implications for drug design.

Authors:  D Gogishvili; E Nittinger; C Margreitter; C Tyrchan
Journal:  J Cheminform       Date:  2021-07-02       Impact factor: 5.514

9.  Approach for the Design of Covalent Protein Kinase Inhibitors via Focused Deep Generative Modeling.

Authors:  Atsushi Yoshimori; Filip Miljković; Jürgen Bajorath
Journal:  Molecules       Date:  2022-01-17       Impact factor: 4.411

10.  Compound dataset and custom code for deep generative multi-target compound design.

Authors:  Thomas Blaschke; Jürgen Bajorath
Journal:  Future Sci OA       Date:  2021-04-30
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