3.1.1 Two traditional processes for discovering small-molecule
candidates
The current research strategies for developing BSMs as drugs are quite
limited. Figure 2 summarizes two traditional processes for
developing small-molecule drugs in recent decades. One of them starts
from the recognized BSMs, and then carries out experiments in different
disease models and at different levels to explore the complex and
variable regulatory networks of small molecules in order to find
potential drug targets [86]. Usually, the BSMs in this research
process are derived from natural products and have exact biological
effects, which would reduce the research cycle and cost. However, it may
also bring some negative effects. For example, the composition of
natural products is complex, and it is difficult to directly determine
which component plays a decisive role. At the same time, BSMs derived
from natural products are often highly toxic and low bio-active with
poor selectivity to targets. In addition, they might have poor stability
and poor organic solubility during the preparation process. To solve
this problem, various approaches combined with bioinformatics have been
achieved with the development of computer-aided drug design (CADD) in
recent years [10, 87, 88]. SwissTargetPrediction, a web server for
target prediction of BSMs, was a good example [89]. It made full use
of the natural concept that ‘similar bioactive molecules are more likely
to share similar targets’ [90] to build a virtual platform, where
the BSMs submitted would be compared with the BSMs existed in the sets
to explore a potential target through molecular fingerprints and
structural similarity measures with known ligands. And this method is
widely used to choose the most sensitive BSMs among the homologous small
molecules and it is more suitable for the optimization process in the
later stages of drug development.
The other way to discover new drugs is based on the existing targets
like kinases. With the development of high-throughput technology in
recent years, this strategy has been combined with computers to
virtually screen potential BSMs for the drug-target interactions (DTIs).
And then the chosen BSMs will be subjected to subsequent pharmacological
tests after being synthesized. This method is called structure-based
drug design (SBDD). It demonstrates strong advantages on saving the
time-consuming and cost of traditional biological experiments. However,
the inevitable challenge it should confront is that the large amount of
the high-dimensional data and background noise is always prepared to
interfere the subsequent data analysis. In this field, different
algorithms have been developed to solve this problem, such as molecular
docking [91], structure-based virtual screening (SBVS) [92], and
molecular dynamics (MD) [93] . AutoDock, a suite of integrated and
interactive software was programmed to predict suitable small molecules
binding with the known structure of the receptor based on the method of
molecular docking, which saves both the time-consuming and high cost for
traditional chemical library screening [94].