A Structure-Based Drug Discovery Paradigm

By Maria Batool, Bilal Ahmad, Sangdun Choi. International Journal of Molecular Sciences, 2019

556 citations recorded by the source corpus.

Structure-based drug design is becoming an essential tool for faster and more cost-efficient lead discovery relative to the traditional method. Genomic, proteomic, and structural studies have provided hundreds of new targets and opportunities for future drug discovery. This situation poses a major problem: the necessity to handle the “big data” generated by combinatorial chemistry. Artificial intelligence (AI) and deep learning play a pivotal role in the analysis and systemization of larger data sets by statistical machine learning methods. Advanced AI-based sophisticated machine learning tools have a significant impact on the drug discovery process including medicinal chemistry. In this review, we focus on the currently available methods and algorithms for structure-based drug design including virtual screening and de novo drug design, with a special emphasis on AI- and deep-learning-based methods used for drug discovery.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ee2d8a15569df3c3413715e8ce682d1fc4820d0c

How researchers see this paper

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In Computational Theory and Mathematics

1. Jianzhu Ma
2. Jian Peng
3. Pushmeet Kohli
4. Ewan Birney
5. Dejing Dou
6. Chanjin Jung
7. Jiaqi Guan
8. Jingyu Lee

In Materials Chemistry

1. Jianzhu Ma
2. S. Velankar
3. Jian Peng
4. Pushmeet Kohli
5. Wesley Wei Qian
6. Chanjin Jung
7. Jiaqi Guan
8. Jingyu Lee

In Molecular Biology

1. Jianzhu Ma
2. S. Velankar
3. Jian Peng
4. Pushmeet Kohli
5. Wesley Wei Qian
6. Ewan Birney
7. Dejing Dou
8. Chanjin Jung

Citations in the corpus

• A review on machine learning approaches and trends in drug discovery (2021, methodology): “…drugs that were designed for a specific purpose for other purposes [78], techniques known as repositioning, of which we saw some examples developed with RF. Structure-based drug design has benefited from machine learning because it is much faster and more cost effective than traditional design [142].”
• Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity (2021, background): “With the development of structural biology and protein structure prediction, especially the recent Alphafold II model [4], there are growing three-dimensional (3D) structure protein data, which enables a new paradigm for structure-based drug discovery [3, 13, 27].”
• AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models (2021, background): “Proteins are essential macromolecules with vital biological functions and, thus, are involved in a wide range of research activities and medical and biotechnological applications, from fighting infectious diseases to tackling environmental pollution (1,2).”
• 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction (2023, background): “Rational drug design against a known protein binding pocket is an efficient and economical approach for finding lead molecules (Anderson, 2003; Batool et al., 2019) and has attracted growing attention from the research community.”
• Retrieval-Augmented Generation for AI-Generated Content: A Survey (2024, unknown): “Based on a constructed RAG system, enhancements are helpful to improve the effectiveness of certain components or the entire pipeline.”
• In silico methods and tools for drug discovery (2021, methodology): “Computational drug discovery methods have been successfully used in the design and identification of drug compounds to treat various diseases, including cancer [228,229], diabetes [230,231], and viral [8,9,232,233] and bacterial infections [128,234 – 240].”