Artificial intelligence for the discovery of novel antimicrobial agents for emerging infectious diseases

Abstract

The search for effective drugs to treat new and existing diseases is a laborious one requiring a large investment of capital, resources, and time. The coronavirus 2019 (COVID-19) pandemic has been a painful reminder of the lack of development of new antimicrobial agents to treat emerging infectious diseases. Artificial intelligence (AI) and other in silico techniques can drive a more efficient, cost-friendly approach to drug discovery by helping move potential candidates with better clinical tolerance forward in the pipeline. Several research teams have developed successful AI platforms for hit identification, lead generation, and lead optimization. In this review, we investigate the technologies at the forefront of spearheading an AI revolution in drug discovery and pharmaceutical sciences.

Keywords: Antimicrobial agents; Artificial intelligence; COVID-19; Infectious diseases.

Graphical abstract

Figure 1

Visualization of protein–protein and protein–chemical graphs. The blue dots represent protein nodes, the green dots represent chemical nodes, the gray dot represents a virus protein, and the lines represent edges in the graph (protein–protein or chemical–protein interactions).

Figure 2

Visualization of node embeddings in two dimensions using t-SNE. The red clusters show how the drugs are clustered, whereas the blue clusters show the clustering of the proteins. Overlap of the blue clusters with the red clusters represent drug–protein interactions.

Figure 3

Bioactive compounds from the Database of Useful (Docking) Decoys Enhanced (DUD-E) database fragmented with eMolFrag. eMolfrag was able to generate an average of six fragments per molecule. eSynth uses beam search techniques to create new drug molecules by combining the building blocks generated by eMolFrag in a chemically comprehensive way. By using fragments generated by eMolFrag, eSynth reconstructed 78.3% of active compounds with a Tanimoto coefficient (TC) of 1.0 and 88.4% with a TC ≥ 0.8. Adapted from Liu et al.

Figure 4

Composition of nontoxic and toxic compounds. The scatter plot shows the frequencies of eMolFrag-extracted chemical fragments from US FDA-approved (nontoxic) and TOXNET (toxic) molecules. The dotted black line is the line of regression, and the gray area represents the corresponding confidence intervals. Examples of three commonly found FDA-approved fragments (piperidine, piperazine, and fluorophenyl) are in green, whereas fragments of more commonly toxic fragments from the TOXNET data set (chlorophenyl, n-butyl, and acetic acid) are in red. Adapted from Figure 8 in Pu et al.