Ligand

Ligands are molecules that bind to specific sites on a target protein. If the binding affinity is strong enough, it often leads to a biological effect. Ligands can be endogenous (originating from within the body) or exogenous small molecules, peptides, or even larger proteins.

Importance of ligands in Computational Drug Discovery

• Target Interaction: Ligands interact with proteins to modulate their function, making them essential for understanding biological pathways and disease mechanisms.
• Binding Affinity: The strength of the interaction between a ligand and its target is crucial for therapeutic efficacy. Computational tools can predict binding affinities, guiding drug design.
• Selectivity: Computational methods can help optimize ligands to enhance binding, selectivity and their bioavailability, which can ultimately lead to better therapeutic outcomes.
• Optimization: Ligands should selectively bind to the target(s) of interest while avoiding other proteins, minimizing side effects.

Key Tools

1. AutoDock: Molecular docking software for predicting ligand-target interactions and binding.

2. BindingDB: Database for measured binding affinities of drug-like molecules.

3. ChEMBL: Database of bioactive molecules and their binding affinities, functional readouts or bioavailability.

4. DeepOrigin’s Ligand prep and property tools: Tools for predicting binding affinities, docking poses, and various molecular properties. Available in Balto and via API.

Literature

1. “Using photolabile ligands in drug discovery and development”

• Publication Date: 2000-02-01

• DOI:10.1016/S0167-7799(99)01402-X

• Summary: Discusses three approaches: photoaffinity labeling, photoactivation and release of ‘caged ligands’, and photoimmobilization of ligands onto surfaces.

2. “Fragment databases from screened ligands for drug discovery (FDSL-DD)”

• Publication Date: 2023-11-01

• DOI:10.1016/j.jmgm.2023.108669

• Summary: Introduces a new method (FDSL-DD) that incorporates fragment characteristics into the drug development process.

3. “Virtual Screening Algorithms in Drug Discovery: A Review Focused on Machine and Deep Learning Methods”

• Publication Date: 2023-05-05

• DOI:10.3390/ddc2020017

• Summary: Reviews algorithms for virtual screening, highlighting the use of machine and deep learning

4. “The Opportunities and Challenges of Peroxisome Proliferator-Activated Receptors Ligands in Clinical Drug Discovery and Development”

• Publication Date: 2018-07-27

• DOI:10.3390/ijms19082189

• Summary: Provides an analysis of 84 types of PPAR ligands and their applications in clinical drug discovery.

5. “Sustainable Drug Discovery of Multi-Target-Directed Ligands for Alzheimer’s Disease”

• Publication Date: 2021-04-08

• DOI:10.1021/acs.jmedchem.1c00048

• Summary: Reports the development of sustainable MTDLs derived from cashew nutshell liquid for Alzheimer’s disease.

6. “Molecular Docking: Principles, Advances, and its Applications in Drug Discovery”

• Publication Date: 2022-09-22

• DOI:10.2174/1570180819666220922103109

• Summary: Reviews the principles and advances in molecular docking and its applications in drug discovery.

7. “NMR in drug discovery: A practical guide to identification and validation of ligands interacting with biological macromolecules”

• Publication Date: 2016-11-01

• DOI:10.1016/j.pnmrs.2016.09.001

• Summary: Introduces the concept of the validation cross to categorize experiments based on information content.

8. “Machine learning approaches and their applications in drug discovery and design”

• Publication Date: 2022-04-15

• DOI:10.1111/cbdd.14057

• Summary: Reviews machine learning approaches used in chemoinformatics for drug discovery.

9. “Network pharmacology: the next paradigm in drug discovery”

• Publication Date: 2008-11-01

• DOI:10.1038/nchembio.118

• Summary: Discusses the role of polypharmacology in tackling efficacy and toxicity in drug development.

10. “Advances in covalent drug discovery”

• Publication Date: 2022-08-25

• DOI:10.1038/s41573-022-00542-z

• Summary: Reviews KRAS(G12C) inhibitors and discusses ligand-first and electrophile-first strategies.

11. “Spotting and designing promiscuous ligands for drug discovery”

• Publication Date: 2016-01-05

• DOI:10.1039/c5cc07506h

• Summary: Discusses a computational tool for identifying promiscuous drug-like ligands.

12. “What is the current value of MM/PBSA and MM/GBSA methods in drug discovery?”

• Publication Date: 2021-06-24

• DOI:10.1080/17460441.2021.1942836

• Summary: Reviews methods for evaluating ligand-receptor interactions and binding free energy.

13. “Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches”

• Publication Date: 2020-10-01

• DOI:10.3390/molecules25204723

• Summary: Analyzes hybrid LB + SB computational schemes in virtual screening.

14. “Electrophilic warheads in covalent drug discovery: an overview”

• Publication Date: 2022-02-06

• DOI:10.1080/17460441.2022.2034783

• Summary: Reviews electrophilic warheads used for protein labeling in chemical biology and medicinal chemistry.

15. “Pharmacophore Modeling in Drug Discovery: Methodology and Current Status”

• Publication Date: 2021-06-29

• DOI:10.18596/jotcsa.927426

• Summary: Discusses the advancements in pharmacophore modeling integrated with other computational methods.