Drug Discovery Methods

Overview of Drug Discovery

Drug discovery is the process of discovering new candidate medications in the field of medicine, biotechnology, and pharmacology. Developing new drugs is always time-consuming and costly. The process is roughly divided into target identification and validation, hit identification, hit to lead, lead optimization, and IND-enabling. To better mimic the complex mechanisms of human diseases and improve the success rate of drug development, a series of drug discovery methods have been developed. However, only 10% of all drugs in human clinical trials become approved drugs according to statistics.

Fig 1. Stages in the drug discovery process. (Lombardino, 2004)

Computational Methods in Drug Discovery

Computer-aided drug design (CADD) has played an important role in the development of therapeutically significant small molecules. In principle, CADD can be divided into two categories, namely structure-based CADD and ligand-based CADD. Structure-based CADD uses molecular docking technology and the three-dimensional structure of the receptor to automatically match the small molecules in the compound database at the binding site, and then use the scoring function based on the molecular force field to calculate the binding energy for the possible binding mode, and finally Get the compound energy ranking. When structural information is unknown, ligand-based CADD is usually preferred.

Medicine Chemistry in Drug Discovery

Medicinal chemistry is a set of highly interdisciplinary sciences that combines synthetic organic chemistry, pharmacology, biochemistry, molecular biology, and various other biological subjects. Organic compounds, especially small organic molecules, are most often used as medicines. New chemical entities are systematically and thoroughly synthesized and modified to make them suitable for therapeutic use. It includes research for the biological activity and properties of existing drugs, that is, understanding their structure-activity relationship (SAR). In summary, medicinal chemistry is focused on the quality aspects of medicines.

Artificial Intelligence (AI) Assisted Drug Discovery

Artificial intelligence (AI) is becoming more and more widely used in the field of the pharmaceutical industry, including new drug discovery, drug repurposing, pharmaceutical productivity improvement, as well as clinical trials. For example, the International Business Machine (IBM) Watson supercomputer allows rapid detection of diseases and the analysis of a patient’s medical information.

The use of artificial intelligence can automatically process large amounts of data to help rational drug design and drug target verification. The recently developed deep learning and related modeling research can be used for the safety and effectiveness evaluation of drug molecules based on big data modeling and analysis. The applications of AI in drug molecular design and drug screening include:

• Prediction of the physicochemical properties
• Prediction of bioactivity
• Prediction of toxicity
• Prediction of the target protein structure
• Predicting drug-protein interactions

Drug Discovery Strategies and Techniques

High-throughput screening (HTS) is the classical method to find a new drug against a special disease target. With the development of theory and technology, a series of drug discovery strategies and techniques have been developed which include but are not limited to:

• Virtual Screening
• High-throughput Screening
• Phenotypic Screening
• Structure-based Drug Design
• Fragment-based Drug Design
• Ligand-based Drug Design
• DNA-encoded Compound Libraries
• CRISPR in Drug Discovery
• Reporter Genes Assays

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Reference

1. Lombardino, J.; Lowe, J. The role of the medicinal chemist in drug discovery—then and now. Nature Reviews Drug Discovery. 2004, 3(10): 853-862.