TropMol-Caipora: A Cloud-Based Web Tool to Predict Cruzain Inhibitors by Machine Learning

TL;DR

This paper introduces TropMol-Caipora, a free online tool that uses machine learning to predict compounds that can inhibit cruzain, a key target for treating Chagas disease.

Contribution

The novel contribution is a publicly accessible cloud-based tool using a Random Forest model to predict cruzain inhibitors with high accuracy.

Findings

Aromaticity, especially with nitrogenous rings, is a key factor in inhibitory activity.

Halogenation positively correlates with compound activity, suggesting it may improve inhibition.

Bicyclic or rigid structures may reduce inhibition efficiency, while molecular accessibility and charge influence activity.

Abstract

Chagas disease (CD) affects approximately 8 million people
and
is classified as a high-priority neglected tropical disease by the
WHO research and development actions. One promising avenue for drug
development for CD is the inhibition of cruzain, a crucial cysteine
protease of T. cruzi and one of the
most extensively studied therapeutic targets. This study aims to construct
a generic molecular screening model for public, online, and free use,
based on pIC50 cruzain predictions using a Random Forest
model. For this, a data set with approximately 8 thousand compounds
and 168 classes of descriptors was used, resulting in more than a
million calculated descriptors. The model achieved R
2 = 0.91 (RMSE = 0.33) for the training set and R
2 = 0.72 (RMSE = 0.55) for the test set. In
5-fold cross-validation, performance remained consistent (R
2 = 0.72 ± 0.01; RMSE = 0.57 ± 0.01).
Some relevant insights were also observed. 1 - Aromaticity was shown
to be a key factor in inhibitory activity. Compounds with nitrogenous
aromatic rings are more likely to be more effective inhibitors. Aromatics
in general also present correlation and structural relevance for an
effective inhibitor. 2 - Halogenation may favor activity. The positive
correlation may suggest that the introduction of halogen atoms may
improve the activity of the compounds. 3 - Bicyclic or very rigid
structures may decrease the inhibition efficiency of the tested candidates.
4 - Molecular accessibility and charge influence activity. Available
in: https://colab.research.google.com/drive/1hotsXPddbJ6E0_hysLT9AqsXL-74Na-z?usp=sharing.

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