Paving the way for small-molecule drug discovery

Abstract

Small-molecule drugs are organic compounds affecting molecular pathways by targeting important proteins, which have a low molecular weight, making them penetrate cells easily. Small-molecule drugs can be developed from leads derived from rational drug design or isolated from natural resources. As commonly used medications, small-molecule drugs can be taken orally, which enter cells to act on intracellular targets. These characteristics make small-molecule drugs promising candidates for drug development, and they are increasingly favored in the pharmaceutical market. Despite the advancements in molecular genetics and effective new processes in drug development, the drugs currently used in clinical practice are inadequate due to their poor efficacy or severe side effects. Therefore, developing new safe and efficient drugs is a top priority for disease control and curing.

Keywords: Small-molecule drugs; cancer; compound; molecular genetics; natural resource.

Figure 1

Workflow of in silico approaches for small molecule drug discovery. The path leading to the development of a new drug is long and complex, representing the convergence of in silico and in vitro screenings and in vitro and in vivo testing and validation, which highlight the need for a faster track in the procedures for drug development to be met by increasing the in silico part of the process, performing via digital computing a series of time-saving evaluations that can greatly simplify the in vitro procedures.

Figure 2

Overview of various discovery platforms for antibacterial drugs. There is a very low probability for a biologically active compounds to succeed from the pre-clinical to clinical phase of drug discovery. For this reason, reliable discovery platforms are needed to continuously produce compounds with antibacterial activity that may be lead compounds for further studies. The currently defined antibiotic discovery platforms are summarized.

Figure 3

Workflow for machine learning in drug discovery. Over the past decade, Machine learning methods have been recognized as the most important tools for extracting chemical compounds with important biological activities from large chemical databases. To understand the physiological and pathological phenomena, it is important to identify ligands that modulate a particular target activity. The main steps of machine learning comprise data collation, chemical descriptor calculation, classifier/model selection, and model validation.

Figure 4

The first-in-class NAE inhibitor, MLN4924. A scheme of the mechanisms of MLN4924 regarding to its therapeutic efficacy and side effect.