Antimalarial Drug Predictions Using Molecular Descriptors and Machine Learning against Plasmodium Falciparum

Abstract

Malaria remains by far one of the most threatening and dangerous illnesses caused by the plasmodium falciparum parasite. Chloroquine (CQ) and first-line artemisinin-based combination treatment (ACT) have long been the drug of choice for the treatment and controlling of malaria; however, the emergence of CQ-resistant and artemisinin resistance parasites is now present in most areas where malaria is endemic. In this work, we developed five machine learning models to predict antimalarial bioactivities of a drug against plasmodium falciparum from the features (i.e., molecular descriptors values) obtained from PaDEL software from SMILES of compounds and compare the machine learning models by experiments with our collected data of 4794 instances. As a consequence, we found that three models amongst the five, namely artificial neural network (ANN), extreme gradient boost (XGB), and random forest (RF), outperform the others in terms of accuracy while observing that, using roughly a quarter of the promising descriptors picked by the feature selection algorithm, the five models achieved equivalent and comparable performance. Nevertheless, the contribution of all molecular descriptors in the models was investigated through the comparison of their rank values by the feature selection algorithm and found that the most potent and relevant descriptors which come from the ‘Autocorrelation’ module contributed more while the ‘Atom type electrotopological state’ contributed the least to the model.

Molecular Descriptors Property Prediction Using Transformer-Based Approach

“…For the AM dataset, we compare our method with the best model Extreme Gradient Boosting (XGB) and Artificial Neural Network (ANN) in [40], and their results are taken directly from this work [40].…”

“…The graph-based model is trained on publicly available antiplasmodial hit compounds and transfer learning from a large dataset was leveraged to improve the performance of the model. Moreover, the work in [40] developed five machine learning models to predict antimalarial bioactivities of a drug against Plasmodium falciparum from the values of the molecular descriptors obtained from SMILES of compounds. They implemented artificial neural networks, support vector machine, random forest, extreme gradient boost, and logistic regression and tested those models on a verified experimental anti-malaria drug compounds dataset.…”

Neural Network Prediction Model of Cocrystal Melting Temperature Based on Molecular Descriptors and Graphs

“…The numerical values of descriptors could quantitatively describe the physical and chemical information of molecules and are the basis of QSPR and Quantitative Structure–Activity Relationship (QSAR) studies, such as those used in ANN to predict certain properties of molecules or to elucidate the interactions between molecules and their surroundings. PaDEL descriptors are open-source software that could be used to compute 1D–3D molecular descriptors, integrating 1875­(1444 1D&2D descriptors and 431 3D descriptors) descriptors and 12 molecular fingerprints. − …”

Machine Learning Prediction of Flavonoid Cocrystal Formation Combined with Experimental Validation

“…These molecular descriptors refer to the measurement of a certain aspect of molecular properties, which could be the physical and chemical properties of molecules or the numerical indicators derived from various algorithms based on the molecular structure, which could quantitatively describe the physical and chemical information on molecules. PaDEL-descriptor is open-source software that could be used to compute 1D–3D molecular descriptors, integrating 1875 (1444 1D & 2D descriptors and 431 3D descriptors) descriptors and 12 molecular fingerprints. − In this study, the software was used to calculate 1545 molecular descriptors of the cocrystals in the flavonoid data set.…”