Abstract
One prominent class of drugs is chemical small molecules (CSMs), but the majority of CSMs are of very low druggable potential. Therefore, it is quite important to predict drug-related properties (druggable properties) for candidate CSMs. Currently, a number of druggable properties (e.g., logP and pKa) can be calculated by in silico methods; still the identification of druggable CSMs is a high-risk task, and new quantitative metrics for the druggable potential of CSMs are increasingly needed. Here, we present normalized bond energy (NBE), a new metric for the above purpose. By applying NBE to the DrugBank CSMs whose properties are largely known, we revealed that NBE is able to describe a number of critical druggable properties including logP, pKa, membrane permeability, blood–brain barrier penetration, and human intestinal absorption. Moreover, given that the human endogenous metabolites can serve as important resources for drug discovery, we applied NBE to the metabolites in the Human Metabolome Database. As a result, NBE showed a significant difference in metabolites from various body fluids and was correlated with some important properties, including melting point and water solubility.
Highlights
Research and development of pharmaceuticals is a resource-consuming and long process with a variety of challenging risks (Szewczak et al, 2020)
We found that normalized bond energy (NBE) score is significantly associated with a number of experimentally identified properties of chemical small molecules (CSMs) in DrugBank
The results showed that the soluble CSMs have smaller NBE scores compared to the insoluble ones
Summary
Research and development of pharmaceuticals is a resource-consuming and long process with a variety of challenging risks (Szewczak et al, 2020). Chemical small molecules (CSMs) represent a big class of drugs which mainly function by binding with disease-related target molecules (Wishart et al, 2018a). Given the huge space of target molecules and CSMs, evaluating the druggable potential of both targets (Jung and Kwon, 2015; Liu et al, 2016; Floris et al, 2018; Sztuba-Solinska et al, 2019) and CSMs (Sun et al, 2016; Ashenden et al, 2017; Chitre et al, 2019; Heitmeier et al, 2019; Bhattacharjee et al, 2020) is one of the key points of drug discovery. For CSMs, it is known that a number of drug-related properties (druggable properties) affect their druggable potential, for example, human intestinal absorption (HIA), blood–brain barrier (BBB) penetration (Blake, 2000), and some pharmacokinetic properties (Ferreira and Andricopulo, 2019). It is crucial to accurately predict druggable properties for an early-phase candidate CSM and large-scale druggable CSM screening. Properties of logP, logD, logS, logW, and pKa can be calculated using the free
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