Role of computer-aided drug design in modern drug discovery

By S. J. Macalino, Vijayakumar Gosu, Sunhye Hong, Sun Choi. Archives of pharmacal research, 2015

615 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/64c5b567ab0c51bcc98c10e65365e051b03af13c

How researchers see this paper

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

In Computational Theory and Mathematics

1. Anastasiia V. Sadybekov
2. Insung S. Choi
3. Sandeepkumar Kothiwale
4. Jens Meiler
5. E. W. Lowe
6. Xianhuan Shen
7. Langhui Li
8. M. Tan

In Molecular Biology

1. Jens Meiler
2. Anastasiia V. Sadybekov
3. Insung S. Choi
4. Sandeepkumar Kothiwale
5. E. W. Lowe
6. Xianhuan Shen
7. Langhui Li
8. M. Tan

In Pharmacology

1. Insung S. Choi
2. Sandeepkumar Kothiwale
3. Jens Meiler
4. E. W. Lowe
5. Giulio Rastelli
6. Luca Pinzi
7. Hyeoncheol Cho

Citations in the corpus

• Introduction to the BioChemical Library (BCL): An Application-Based Open-Source Toolkit for Integrated Cheminformatics and Machine Learning in Computer-Aided Drug Discovery (2022, methodology): “Computer-aided drug discovery (CADD) methods are routinely employed to improve the ef fi ciency of hit identi fi cation and lead optimization (Macalino et al., 2015; Usha et al., 2017).”
• PointSite: a point cloud segmentation tool for identification of protein ligand binding atoms (2019, neutral): “Till now, various approaches have been developed for the identi-fication of protein LBS, which are comprehensively reviewed and summarized in [33, 48, 40, 32, 31, 15, 29, 9, 39, 4, 34, 46].”
• Molecular Docking: Shifting Paradigms in Drug Discovery (2019, background): “The possibilities offered by molecular docking in drug discovery are well established [3,5,7,18,47,49,50,63].”
• Computational Approaches in Preclinical Studies on Drug Discovery and Development (2020, methodology): “In recent years, structure-based methods have been widely used, for instance, to predict the binding patterns of substrates ( Macalino et al., 2015), conformational changes in enzymes (Cheng T. et al., 2012), and their catalytic effects on physiological systems (Cui and Karplus, 2003), to evaluate…”
• Computational approaches streamlining drug discovery (2023, background): “There have been success stories along the way 5 and, in general, computer-assisted approaches have become an integral, yet modest, part of the drug discovery process 6,7 .”
• Enhanced Deep‐Learning Prediction of Molecular Properties via Augmentation of Bond Topology (2018, methodology): “Chemists have developed various methods for computationally simulating these interactions with or without information on the actual structure of the binding pocket, based on physicochemical calculations and chemical-similarity analyses.(4,5) Besides the ongoing efforts on the computational-chemistry”