The Virtual Screening of the Drug Protein with a Few Crystal Structures Based on the Adaboost-SVM

A review of machine learning-based methods for predicting drug-target interactions

The prediction of drug-target interactions (DTI) is a crucial preliminary stage in drug discovery and development, given the substantial risk of failure and the prolonged validation period associated with in vitro and in vivo experiments. In the contemporary landscape, various machine learning-based methods have emerged as indispensable tools for DTI prediction. This paper begins by placing emphasis on the data representation employed by these methods, delineating five representations for drugs and four for proteins. The methods are then categorized into traditional machine learning-based approaches and deep learning-based ones, with a discussion of representative approaches in each category and the introduction of a novel taxonomy for deep neural network models in DTI prediction. Additionally, we present a synthesis of commonly used datasets and evaluation metrics to facilitate practical implementation. In conclusion, we address current challenges and outline potential future directions in this research field.

Disease-Ligand Identification Based on Flexible Neural Tree

In order to screen the disease-related compounds of a traditional Chinese medicine prescription in network pharmacology research accurately, a new virtual screening method based on flexible neural tree (FNT) model, hybrid evolutionary method and negative sample selection algorithm is proposed. A novel hybrid evolutionary algorithm based on the Grammar-guided genetic programming and salp swarm algorithm is proposed to infer the optimal FNT. According to hypertension, diabetes, and Corona Virus Disease 2019, disease-related compounds are collected from the up-to-date literatures. The unrelated compounds are chosen by negative sample selection algorithm. ECFP6, MACCS, Macrocycle, and RDKit are utilized to numerically characterize the chemical structure of each compound collected, respectively. The experiment results show that our proposed method performs better than classical classifiers [Support Vector Machine (SVM), random forest (RF), AdaBoost, decision tree (DT), Gradient Boosting Decision Tree (GBDT), KNN, logic regression (LR), and Naive Bayes (NB)], up-to-date classifier (gcForest), and deep learning method (forgeNet) in terms of AUC, ROC, TPR, FPR, Precision, Specificity, and F1. MACCS method is suitable for the maximum number of classifiers. All methods perform poorly with ECFP6 molecular descriptor.

Positive Predictive Value Surfaces as a Complementary Tool to Assess the Performance of Virtual Screening Methods

BACKGROUND

Since their introduction in the virtual screening field, Receiver Operating Characteristic (ROC) curve-derived metrics have been widely used for benchmarking of computational methods and algorithms intended for virtual screening applications. Whereas in classification problems, the ratio between sensitivity and specificity for a given score value is very informative, a practical concern in virtual screening campaigns is to predict the actual probability that a predicted hit will prove truly active when submitted to experimental testing (in other words, the Positive Predictive Value - PPV). Estimation of such probability is however, obstructed due to its dependency on the yield of actives of the screened library, which cannot be known a priori.

OBJECTIVE

To explore the use of PPV surfaces derived from simulated ranking experiments (retrospective virtual screening) as a complementary tool to ROC curves, for both benchmarking and optimization of score cutoff values.

METHODS

The utility of the proposed approach is assessed in retrospective virtual screening experiments with four datasets used to infer QSAR classifiers: inhibitors of Trypanosoma cruzi trypanothione synthetase; inhibitors of Trypanosoma brucei N-myristoyltransferase; inhibitors of GABA transaminase and anticonvulsant activity in the 6 Hz seizure model.

RESULTS

Besides illustrating the utility of PPV surfaces to compare the performance of machine learning models for virtual screening applications and to select an adequate score threshold, our results also suggest that ensemble learning provides models with better predictivity and more robust behavior.

CONCLUSION

PPV surfaces are valuable tools to assess virtual screening tools and choose score thresholds to be applied in prospective in silico screens. Ensemble learning approaches seem to consistently lead to improved predictivity and robustness.

Deep Learning in Virtual Screening: Recent Applications and Developments

Drug discovery is a cost and time-intensive process that is often assisted by computational methods, such as virtual screening, to speed up and guide the design of new compounds. For many years, machine learning methods have been successfully applied in the context of computer-aided drug discovery. Recently, thanks to the rise of novel technologies as well as the increasing amount of available chemical and bioactivity data, deep learning has gained a tremendous impact in rational active compound discovery. Herein, recent applications and developments of machine learning, with a focus on deep learning, in virtual screening for active compound design are reviewed. This includes introducing different compound and protein encodings, deep learning techniques as well as frequently used bioactivity and benchmark data sets for model training and testing. Finally, the present state-of-the-art, including the current challenges and emerging problems, are examined and discussed.