Impact of Molecular Descriptors on Computational Models

Antimicrobial Activity Classification of Imidazolium Derivatives Predicted by Artificial Neural Networks

PURPOSE

This study assesses the Multilayer Perceptron (MLP) neural network, complemented by other Machine Learning techniques (CART, PCA), in predicting the antimicrobial activity of 140 newly designed imidazolium chlorides against Klebsiella pneumoniae before synthesis. Emphasis is on leveraging molecular properties for predictive analysis.

METHODS

Classification and regression decision trees (CART) identified the top 200 predictive molecular descriptors. Principal Component Analysis (PCA) reduced these descriptors to 5 components, retaining 99.57% of raw data information. Antimicrobial activity, categorized as high or low, was based on experimentally proven minimal inhibitory concentration (MIC), with a cut-point at MIC = 0.856 mol/L. A 12-fold cross-validation trained the MLP (architecture 5-12-2 with 5 Principal Components).

RESULTS

The MLP exhibited commendable performance, achieving almost 90% correct classifications across learning, validation, and test sets, outperforming models without PCA dimension reduction. Key metrics, including accuracy (0.907), sensitivity (0.905), specificity (0.909), and precision (0.891), were notably high. These results highlight the MLP model's efficacy with PCA as a high-quality classifier for determining antimicrobial activity.

CONCLUSIONS

The study concludes that the MLP neural network, along with CART and PCA, is a robust tool for predicting the antimicrobial activity class of imidazolium chlorides against Klebsiella pneumoniae. CART and PCA, used in this study, allowed input variable reduction without significant information loss. High classification accuracy and associated metrics affirm the method's potential utility in pre-synthesis assessments, offering valuable insights for antimicrobial compound design.

Integrated machine learning-based virtual screening and biological evaluation for identification of potential inhibitors against cathepsin K

Cathepsin K is a type of cysteine proteinase that is primarily expressed in osteoclasts and has a key role in the breakdown of bone matrix protein during bone resorption. Many studies suggest that the deficiency of cathepsin K is concomitant with a suppression of osteoclast functioning, therefore rendering the resorptive properties of cathepsin K the most prominent target for osteoporosis. This innovative work has identified a novel anti-osteoporotic agent against Cathepsin K by using a comparison of machine learning and deep learning-based virtual screening followed by their biological evaluation. Out of ten shortlisted compounds, five of the compounds (JFD02945, JFD02944, RJC01981, KM08968 and SB01934) exhibit more than 50% inhibition of the Cathepsin K activity at 0.1 μM concentration and are considered to have a promising inhibitory effect against Cathepsin K. The comprehensive docking, MD simulation, and MM/PBSA investigations affirm the stable and effective interaction of these compounds with Cathepsin K to inhibit its function. Furthermore, the compounds RJC01981, KM08968 and SB01934 are represented to have promising anti-osteoporotic properties for the management of osteoporosis owing to their significantly well predicted ADMET properties.

A knowledge-guided pre-training framework for improving molecular representation learning

Learning effective molecular feature representation to facilitate molecular property prediction is of great significance for drug discovery. Recently, there has been a surge of interest in pre-training graph neural networks (GNNs) via self-supervised learning techniques to overcome the challenge of data scarcity in molecular property prediction. However, current self-supervised learning-based methods suffer from two main obstacles: the lack of a well-defined self-supervised learning strategy and the limited capacity of GNNs. Here, we propose Knowledge-guided Pre-training of Graph Transformer (KPGT), a self-supervised learning framework to alleviate the aforementioned issues and provide generalizable and robust molecular representations. The KPGT framework integrates a graph transformer specifically designed for molecular graphs and a knowledge-guided pre-training strategy, to fully capture both structural and semantic knowledge of molecules. Through extensive computational tests on 63 datasets, KPGT exhibits superior performance in predicting molecular properties across various domains. Moreover, the practical applicability of KPGT in drug discovery has been validated by identifying potential inhibitors of two antitumor targets: hematopoietic progenitor kinase 1 (HPK1) and fibroblast growth factor receptor 1 (FGFR1). Overall, KPGT can provide a powerful and useful tool for advancing the artificial intelligence (AI)-aided drug discovery process.

Developing reliable classification of dual IDO1/TDO inhibitors using data fusion and majority voting

Indoleamine 2,3-dioxygenase 1 (IDO1) and tryptophan 2,3-dioxygenase (TDO) are promising dual-targeting inhibitors in cancer and neurodegenerative diseases treatment. Data fusion of receptor-based and ligand-based information of dual IDO1/TDO inhibitors were employed for active/inactive classification performance. A reliable decision making procedure was used here to identify active/inactive dual IDO1/TDO inhibitors using majority voting method and pools of individual classifications instead of individual models. All classification models were validated using prediction set, cross-validation and y-scrambling tests. The classification outcomes indicate that the sensitivity, specificity, precision, accuracy, G-mean and F1 score values increases up to ∼90% using data fusion and majority voting method. Compare to individual classification models with a single prediction point, the majority voting method has more reliable results due to the integration of the pool of individual classification models. This classification strategy may lead to more reliable identification of active/inactive dual-targeting inhibitors in cancer immunotherapy.Communicated by Ramaswamy H. Sarma.

DeepADRA2A: predicting adrenergic α2a inhibitors using deep learning

Adrenergic α2a (ADRA2A) receptors play a crucial role in modulating various physiological actions, thereby influencing the proper functioning of different systems in the body. ADRA2A regulation is associated with a wide range of effects, including alterations in blood pressure, hypertension, heightened heart rate, etc. Inhibition of these receptors results in the release of noradrenaline, leading to heightened physiological activity, improved alertness, reduced blood pressure, and alleviation of hypertension. Conventional approaches for identifying ADRA2A inhibitors are burdened with high costs, labor-intensive procedures, and time-consuming processes. In light of these challenges, leveraging the power of artificial intelligence offers a promising solution for drug discovery and development. This study endeavors to harness the potential of artificial intelligence to develop robust models capable of accurately predicting ADRA2A inhibitors and non-inhibitors. By doing so, we aim to streamline and expedite the identification of potential drug candidates in this domain. In this study, we employed four different machine learning (ML) and deep learning (DL) algorithms to develop prediction models based on various molecular descriptors (1D, 2D, and molecular fingerprints). Among these models, the DL-based prediction model demonstrated superior performance, achieving accuracies of 98.25% and 97.23% on the training and test datasets, respectively. These results underscore the efficacy of DL-based model, as a highly effective tool for predicting ADRA2A inhibitors. The model is made available at https://github.com/PGlab-NIPER/DeepADRA2A.git.Communicated by Ramaswamy H. Sarma.

A machine learning-based quantitative model (LogBB_Pred) to predict the blood-brain barrier permeability (logBB value) of drug compounds

MOTIVATION

Efficient assessment of the blood-brain barrier (BBB) penetration ability of a drug compound is one of the major hurdles in central nervous system drug discovery since experimental methods are costly and time-consuming. To advance and elevate the success rate of neurotherapeutic drug discovery, it is essential to develop an accurate computational quantitative model to determine the absolute logBB value (a logarithmic ratio of the concentration of a drug in the brain to its concentration in the blood) of a drug candidate.

RESULTS

Here, we developed a quantitative model (LogBB_Pred) capable of predicting a logBB value of a query compound. The model achieved an R2 of 0.61 on an independent test dataset and outperformed other publicly available quantitative models. When compared with the available qualitative (classification) models that only classified whether a compound is BBB-permeable or not, our model achieved the same accuracy (0.85) with the best qualitative model and far-outperformed other qualitative models (accuracies between 0.64 and 0.70). For further evaluation, our model, quantitative models, and the qualitative models were evaluated on a real-world central nervous system drug screening library. Our model showed an accuracy of 0.97 while the other models showed an accuracy in the range of 0.29-0.83. Consequently, our model can accurately classify BBB-permeable compounds as well as predict the absolute logBB values of drug candidates.

AVAILABILITY AND IMPLEMENTATION

Web server is freely available on the web at http://ssbio.cau.ac.kr/software/logbb_pred/. The data used in this study are available to download at http://ssbio.cau.ac.kr/software/logbb_pred/dataset.zip.

Multivariate QSAR, similarity search and ADMET studies based in a set of methylamine derivatives described as dopamine transporter inhibitors

The dopamine transporter (DAT), responsible for the regulation of dopaminergic neurotransmission, is implicated in the etiology of several neuropsychiatric disorders which, in turn, have contributed to high rates of disability and numerous deaths in recent years, significantly impacting the global health system. Although the research for new drugs for the treatment of neuropsychiatric disorders has evolved in recent years, the availability of DAT-selective drugs that do not generate the same psychostimulant effects observed in drugs of abuse remains scarce. Therefore, we performed a QSAR study based on a dataset of 36 methylamine derivatives described as DAT inhibitors. The model was obtained based only in descriptors derived from 2D structures, and it was validated and generated satisfactory results considering the metrics used for internal and external validation. Subsequently, a virtual screening step also based on 2D similarity was performed, where it was possible to identify a total of 1157 compounds. After a series of reductions of the set using toxicity filters, applicability domain evaluation, and pharmacokinetic properties in silico assessment, seven hit compounds were selected as the most promising to be used, in future studies, as new scaffolds for the development of new DAT inhibitors.

Integrated data-driven and experimental approaches to accelerate lead optimization targeting SARS-CoV-2 main protease

Identification of potential therapeutic candidates can be expedited by integrating computational modeling with domain aware machine learning (ML) models followed by experimental validation in an iterative manner. Generative deep learning models can generate thousands of new candidates, however, their physiochemical and biochemical properties are typically not fully optimized. Using our recently developed deep learning models and a scaffold as a starting point, we generated tens of thousands of compounds for SARS-CoV-2 M^pro that preserve the core scaffold. We utilized and implemented several computational tools such as structural alert and toxicity analysis, high throughput virtual screening, ML-based 3D quantitative structure-activity relationships, multi-parameter optimization, and graph neural networks on generated candidates to predict biological activity and binding affinity in advance. As a result of these combined computational endeavors, eight promising candidates were singled out and put through experimental testing using Native Mass Spectrometry and FRET-based functional assays. Two of the tested compounds with quinazoline-2-thiol and acetylpiperidine core moieties showed IC[Formula: see text] values in the low micromolar range: [Formula: see text] [Formula: see text]M and 3.41±0.0015 [Formula: see text]M, respectively. Molecular dynamics simulations further highlight that binding of these compounds results in allosteric modulations within the chain B and the interface domains of the M^pro. Our integrated approach provides a platform for data driven lead optimization with rapid characterization and experimental validation in a closed loop that could be applied to other potential protein targets.

Large-scale evaluation of k-fold cross-validation ensembles for uncertainty estimation

It is insightful to report an estimator that describes how certain a model is in a prediction, additionally to the prediction alone. For regression tasks, most approaches implement a variation of the ensemble method, apart from few exceptions. Instead of a single estimator, a group of estimators yields several predictions for an input. The uncertainty can then be quantified by measuring the disagreement between the predictions, for example by the standard deviation. In theory, ensembles should not only provide uncertainties, they also boost the predictive performance by reducing errors arising from variance. Despite the development of novel methods, they are still considered the "golden-standard" to quantify the uncertainty of regression models. Subsampling-based methods to obtain ensembles can be applied to all models, regardless whether they are related to deep learning or traditional machine learning. However, little attention has been given to the question whether the ensemble method is applicable to virtually all scenarios occurring in the field of cheminformatics. In a widespread and diversified attempt, ensembles are evaluated for 32 datasets of different sizes and modeling difficulty, ranging from physicochemical properties to biological activities. For increasing ensemble sizes with up to 200 members, the predictive performance as well as the applicability as uncertainty estimator are shown for all combinations of five modeling techniques and four molecular featurizations. Useful recommendations were derived for practitioners regarding the success and minimum size of ensembles, depending on whether predictive performance or uncertainty quantification is of more importance for the task at hand.

DE-INTERACT: A machine-learning-based predictive tool for the drug-excipient interaction study during product development-Validation through Paracetamol and Vanillin as a case study

The compatibility of drugs with excipients plays a crucial role in the prospective stability of pharmaceutical formulations. Apart from real-time stability studies, conventional analytical tools like DSC, FTIR, NMR, and chromatography help identify the possibilities of drug-excipient interactions. Machine learning can assist in developing a predictive tool for drug-excipient incompatibility. In the present work, PubChem Fingerprint is employed as the descriptor of compounds that thoroughly represents the drug's and excipient's chemistry. The 881-bit binary fingerprints of each drug and excipient make 1762 inputs, and one categorical output makes an instance in the dataset. A dataset of more than 3500 instances of drugs and excipients is carefully selected from peer-reviewed research papers. Rigorous training of the Artificial Neural Network (ANN) model was performed with maximum validation accuracy, minimum validation loss, and maximum validation precision as the checkpoints. The machine learning model (DE-Interact) was trained, achieving training and validation accuracies of 0.9930 and 0.9161, respectively. The performance of the DE-Interact model was evaluated by confirming three incompatible predictions by conventional analytical tools. Paracetamol with vanillin, paracetamol with methylparaben, and brinzolamide with polyethyleneglycol are these instances which are predicted as incompatible by the DE-Interact. DSC, FTIR, HPTLC, and HPLC analysis confirm the prediction. The present work offers a reliable DE-Interact tool for quick referencing while selecting excipients in formulation design.

A novel nonlinear time-varying sigmoid transfer function in binary whale optimization algorithm for descriptors selection in drug classification

In computational chemistry, the high-dimensional molecular descriptors contribute to the curse of dimensionality issue. Binary whale optimization algorithm (BWOA) is a recently proposed metaheuristic optimization algorithm that has been efficiently applied in feature selection. The main contribution of this paper is a new version of the nonlinear time-varying Sigmoid transfer function to improve the exploitation and exploration activities in the standard whale optimization algorithm (WOA). A new BWOA algorithm, namely BWOA-3, is introduced to solve the descriptors selection problem, which becomes the second contribution. To validate BWOA-3 performance, a high-dimensional drug dataset is employed. The proficiency of the proposed BWOA-3 and the comparative optimization algorithms are measured based on convergence speed, the length of the selected feature subset, and classification performance (accuracy, specificity, sensitivity, and f-measure). In addition, statistical significance tests are also conducted using the Friedman test and Wilcoxon signed-rank test. The comparative optimization algorithms include two BWOA variants, binary bat algorithm (BBA), binary gray wolf algorithm (BGWOA), and binary manta-ray foraging algorithm (BMRFO). As the final contribution, from all experiments, this study has successfully revealed the superiority of BWOA-3 in solving the descriptors selection problem and improving the Amphetamine-type Stimulants (ATS) drug classification performance.

A novel strategy for designing the magic shotguns for distantly related target pairs

Due to its promising capacity in improving drug efficacy, polypharmacology has emerged to be a new theme in the drug discovery of complex disease. In the process of novel multi-target drugs (MTDs) discovery, in silico strategies come to be quite essential for the advantage of high throughput and low cost. However, current researchers mostly aim at typical closely related target pairs. Because of the intricate pathogenesis networks of complex diseases, many distantly related targets are found to play crucial role in synergistic treatment. Therefore, an innovational method to develop drugs which could simultaneously target distantly related target pairs is of utmost importance. At the same time, reducing the false discovery rate in the design of MTDs remains to be the daunting technological difficulty. In this research, effective small molecule clustering in the positive dataset, together with a putative negative dataset generation strategy, was adopted in the process of model constructions. Through comprehensive assessment on 10 target pairs with hierarchical similarity-levels, the proposed strategy turned out to reduce the false discovery rate successfully. Constructed model types with much smaller numbers of inhibitor molecules gained considerable yields and showed better false-hit controllability than before. To further evaluate the generalization ability, an in-depth assessment of high-throughput virtual screening on ChEMBL database was conducted. As a result, this novel strategy could hierarchically improve the enrichment factors for each target pair (especially for those distantly related/unrelated target pairs), corresponding to target pair similarity-levels.

Concepts and applications of chemical fingerprint for hit and lead screening

Molecular fingerprints are used to represent chemical (structural, physicochemical, etc.) properties of large-scale chemical sets in a low computational cost way. They have a prominent role in transforming chemical data sets into consistent input formats (bit strings or numeric values) suitable for in silico approaches. In this review, we summarize and classify common and state-of-the-art fingerprints into eight different types (dictionary based, circular, topological, pharmacophore, protein-ligand interaction, shape based, reinforced, and multi). We also highlight applications of fingerprints in early drug research and development (R&D). Thus, this review provides a guide for the selection of appropriate fingerprints of compounds (or ligand-protein complexes) for use in drug R&D.

Hybrid consensus and k-nearest neighbours (kNN) strategies to classify dual BRD4/PLK1 inhibitors

A novel decision-making procedure is proposed here for the first time to identify active/inactive and selective/non-selective dual inhibitors using consensus approaches and pools of k-nearest neighbours (kNN) classifications instead of individual models. Dual BRD4/PLK1 inhibition with adequate selectivity is a potential therapeutic strategy for targeting tumour cells in high-risk patients. We report the unique way to identify both active and selective dual BRD4/PLK1 inhibitors using consensus and kNN strategies together with two sources of receptor-based and ligand-based information which are the ranked binding energies of residues and important molecular features, respectively. The results of consensus approaches were compared with the results of individual kNN models. The chemical space similarity was measured using three different distance functions to increase the reliability. All activity and selectivity classification models were validated using cross-validation and y-randomization tests. The outcomes show that consensus approaches can increase the reliability and accuracy of active/inactive or selective/non-selective detections up to 90%. Consensus approaches also reached more balanced values of sensitivity and specificity compared to the individual kNN models because of the compensation in the integration of diverse sources of information.

Machine Learning in Chemoinformatics and Medicinal Chemistry

In chemoinformatics and medicinal chemistry, machine learning has evolved into an important approach. In recent years, increasing computational resources and new deep learning algorithms have put machine learning onto a new level, addressing previously unmet challenges in pharmaceutical research. In silico approaches for compound activity predictions, de novo design, and reaction modeling have been further advanced by new algorithmic developments and the emergence of big data in the field. Herein, novel applications of machine learning and deep learning in chemoinformatics and medicinal chemistry are reviewed. Opportunities and challenges for new methods and applications are discussed, placing emphasis on proper baseline comparisons, robust validation methodologies, and new applicability domains. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Natural product drug discovery in the artificial intelligence era

Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.

Block-wise exploration of molecular descriptors with Multi-block Orthogonal Component Analysis (MOCA)

Data tables for machine learning and structure‐activity relationship modelling (QSAR) are often naturally organized in blocks of data, where multiple molecular representations or sets of descriptors form the blocks. Multi‐block Orthogonal Component Analysis (MOCA), a new analytical tool, can be used to explore such data structures in a single model, identifying principal components that are unique to a single block or joint over multiple blocks. We applied MOCA to two sets of 550 and 300 molecules and up to 9213 molecular descriptors organized in 11 blocks. The MOCA models reveal relationships between the blocks and overarching trends across the whole dataset. Based on the MOCA joint components, we propose a quantitative metric for the redundancy of blocks, useful for a priori block‐wise feature selection or evaluation of new molecular representations. The second data set includes 7 ecotoxicological study endpoints for crop protection chemicals, for which we (re‐)discovered some general trends and linked them to molecular properties. Using a single MOCA model we estimated the predictive potential of each block and the model‐ability of the target block.

Water Activity Prediction in Sugar and Polyol Systems Using Theoretical Molecular Descriptors

Water activity is a key factor in the development of pharmaceutical, cosmetic, and food products. In aqueous solutions of nonelectrolytes, the Norrish model provides a simple and effective way to evaluate this quantity. However, it contains a parameter, known as the Norrish constant, that must be estimated from experimental data. In this study, a new strategy is proposed for the prediction of water activity in the absence of experimental information, based on the use of theoretical molecular descriptors for characterizing the effects of a solute. This approach was applied to the evaluation of water activity in the presence of sugars (glucose, fructose, xylose, sucrose) and polyols (sorbitol, xylitol, glycerol, erythritol). The use of two descriptors related to the constitutional and connectivity properties of the solutes was first investigated. Subsequently, a new theoretical descriptor, named the global information index (G), was developed. By using this index, the water activity curves in the binary systems were reconstructed. The positive results obtained support the proposed strategy, as well as the possibility of including, in a single information index, the main molecular features of a solute that determine its effects on water activity.

Molecular Scaffold Hopping via Holistic Molecular Representation

Molecular descriptors encode a variety of molecular representations for computer-assisted drug discovery. Here, we focus on the Weighted Holistic Atom Localization and Entity Shape (WHALES) descriptors, which were originally designed for scaffold hopping from natural products to synthetic molecules. WHALES descriptors capture molecular shape and partial charges simultaneously. We introduce the key aspects of the WHALES concept and provide a step-by-step guide on how to use these descriptors for virtual compound screening and scaffold hopping. The results presented can be reproduced by using the code freely available from URL: github.com/ETHmodlab/scaffold_hopping_whales .

Learning Molecular Representations for Medicinal Chemistry

The accurate modeling and prediction of small molecule properties and bioactivities depend on the critical choice of molecular representation. Decades of informatics-driven research have relied on expert-designed molecular descriptors to establish quantitative structure-activity and structure-property relationships for drug discovery. Now, advances in deep learning make it possible to efficiently and compactly learn molecular representations directly from data. In this review, we discuss how active research in molecular deep learning can address limitations of current descriptors and fingerprints while creating new opportunities in cheminformatics and virtual screening. We provide a concise overview of the role of representations in cheminformatics, key concepts in deep learning, and argue that learning representations provides a way forward to improve the predictive modeling of small molecule bioactivities and properties.

Distributed and multicore QuBiLS-MIDAS software v2.0: Computing chiral, fuzzy, weighted and truncated geometrical molecular descriptors based on tensor algebra

Advances to the distributed, multi-core and fully cross-platform QuBiLS-MIDAS software v2.0 (http://tomocomd.com/qubils-midas) are reported in this article since the v1.0 release. The QuBiLS-MIDAS software is the only one that computes atom-pair and alignment-free geometrical MDs (3D-MDs) from several distance metrics other than the Euclidean distance, as well as alignment-free 3D-MDs that codify structural information regarding the relations among three and four atoms of a molecule. The most recent features added to the QuBiLS-MIDAS software v2.0 are related (a) to the calculation of atomic weightings from indices based on the vertex-degree invariant (e.g., Alikhanidi index); (b) to consider central chirality during the molecular encoding; (c) to use measures based on clustering methods and statistical functions to codify structural information among more than two atoms; (d) to the use of a novel method based on fuzzy membership functions to spherically truncate inter-atomic relations; and (e) to the use of weighted and fuzzy aggregation operators to compute global 3D-MDs according to the importance and/or interrelation of the atoms of a molecule during the molecular encoding. Moreover, a novel module to compute QuBiLS-MIDAS 3D-MDs from their headings was also developed. This module can be used either by the graphical user interface or by means of the software library. By using the library, both the predictive models built with the QuBiLS-MIDAS 3D-MDs and the QuBiLS-MIDAS 3D-MDs calculation can be embedded in other tools. A set of predefined QuBiLS-MIDAS 3D-MDs with high information content and low redundancy on a set comprised of 20,469 compounds is also provided to be employed in further cheminformatics tasks. This set of predefined 3D-MDs evidenced better performance than all the universe of Dragon (v5.5) and PaDEL 0D-to-3D MDs in variability studies, whereas a linear independence study proved that these QuBiLS-MIDAS 3D-MDs codify chemical information orthogonal to the Dragon 0D-to-3D MDs. This set of predefined 3D-MDs would be periodically updated as long as new results be achieved. In general, this report highlights our continued efforts to provide a better tool for a most suitable characterization of compounds, and in this way, to contribute to obtaining better outcomes in future applications.

Targeting HIV/HCV Coinfection Using a Machine Learning-Based Multiple Quantitative Structure-Activity Relationships (Multiple QSAR) Method

Human immunodeficiency virus type-1 and hepatitis C virus (HIV/HCV) coinfection occurs when a patient is simultaneously infected with both human immunodeficiency virus type-1 (HIV-1) and hepatitis C virus (HCV), which is common today in certain populations. However, the treatment of coinfection is a challenge because of the special considerations needed to ensure hepatic safety and avoid drug–drug interactions. Multitarget inhibitors with less toxicity may provide a promising therapeutic strategy for HIV/HCV coinfection. However, the identification of one molecule that acts on multiple targets simultaneously by experimental evaluation is costly and time-consuming. In silico target prediction tools provide more opportunities for the development of multitarget inhibitors. In this study, by combining Naïve Bayes (NB) and support vector machine (SVM) algorithms with two types of molecular fingerprints, MACCS and extended connectivity fingerprints 6 (ECFP6), 60 classification models were constructed to predict compounds that were active against 11 HIV-1 targets and four HCV targets based on a multiple quantitative structure–activity relationships (multiple QSAR) method. Five-fold cross-validation and test set validation were performed to measure the performance of the 60 classification models. Our results show that the 60 multiple QSAR models appeared to have high classification accuracy in terms of the area under the ROC curve (AUC) values, which ranged from 0.83 to 1 with a mean value of 0.97 for the HIV-1 models and from 0.84 to 1 with a mean value of 0.96 for the HCV models. Furthermore, the 60 models were used to comprehensively predict the potential targets of an additional 46 compounds, including 27 approved HIV-1 drugs, 10 approved HCV drugs and nine selected compounds known to be active against one or more targets of HIV-1 or HCV. Finally, 20 hits, including seven approved HIV-1 drugs, four approved HCV drugs, and nine other compounds, were predicted to be HIV/HCV coinfection multitarget inhibitors. The reported bioactivity data confirmed that seven out of nine compounds actually interacted with HIV-1 and HCV targets simultaneously with diverse binding affinities. The remaining predicted hits and chemical-protein interaction pairs with the potential ability to suppress HIV/HCV coinfection are worthy of further experimental investigation. This investigation shows that the multiple QSAR method is useful in predicting chemical-protein interactions for the discovery of multitarget inhibitors and provides a unique strategy for the treatment of HIV/HCV coinfection.