pBRICS: A Novel Fragmentation Method for Explainable Property Prediction of Drug-Like Small Molecules

Generative artificial intelligence algorithms have shown
to be
successful in exploring large chemical spaces and designing novel
and diverse molecules. There has been considerable interest in developing
predictive models using artificial intelligence for drug-like properties,
which can potentially reduce the late-stage attrition of drug candidates
or predict the properties of novel AI-designed molecules. Concurrently,
it is important to understand the contribution of functional groups
toward these properties and modify them to obtain property-optimized
lead compounds. As a result, there is an increasing interest in the
development of explainable property prediction models. However, current
explainable approaches are mostly atom-based, where, often, only a
fraction of a fragment is shown to be significant. To address the
above challenges, we have developed a novel domain-aware molecular
fragmentation approach termed post-processing of BRICS (pBRICS), which
can fragment small molecules into their functional groups. Multitask
models were developed to predict various properties, including the
absorption, distribution, metabolism, excretion, and toxicity (ADMET)
properties. The fragment importance was explained using the gradient-weighted
class activation mapping (Grad-CAM) approach. The method was validated
on data sets of experimentally available matched molecular pairs (MMPs).
The explanations from the model can be useful for medicinal chemists
to identify the fragments responsible for poor drug-like properties
and optimize the molecule. The explainability approach was also used
to identify the reason behind false positive and false negative MMP
predictions. Based on evidence from the existing literature and our
analysis, some of these mispredictions were justified. We propose
that the quantity, quality, and diversity of the training data will
improve the accuracy of property prediction algorithms for novel molecules.