Structure-ADME relationship: still a long way to go?

Radioactive ADME Demonstrates ARV-110's High Druggability Despite Low Oral Bioavailability

Proteolysis-targeting chimeras (PROTACs) have emerged as potentially effective therapeutic medicines, but their high molecular weight and poor solubility directly impact their oral bioavailability. This work synthesized 14C-labeled bavdegalutamide (ARV-110) as a model compound of PROTACs to evaluate its ADME features. Compared with targeted antitumor drugs, the use of food increased oral bioavailability of ARV-110 in rats from 10.75% to 20.97%, which is still undesirable. However, the therapeutic effect of ARV-110 at a low dose was much better than that of enzalutamide, demonstrating the specific catalytic medicinal properties of PROTACs. Moreover, the specific distribution of ARV-110 in subcutaneous prostate tumors was determined by quantitative whole-body autoradiography (QWBA). Notably, the specificity and activity of PROTACs take precedence over their oral absorption, and high oral bioavailability is not necessary to produce excellent therapeutic effects. This work presents a roadmap for developing future PROTAC medications from a radioactive drug metabolism and pharmacokinetics (DMPK) perspective.

Modeling of small molecule's affinity to phospholipids using IAM-HPLC and QSRR approach enhanced by similarity-based machine algorithms

Immobilized artificial membrane chromatography (IAM) has been proposed as a more biosimilar alternative to classical lipophilicity measurement. Determination of small molecule's affinity to phospholipids can be supported for predicting their behavior in the human body. Therefore, a better understanding of the molecular interaction mechanism between small xenobiotics and phospholipids can accelerate drug discovery. Here, the quantitative structure-retention relationships (QSRR) approach was integrated with mechanistic descriptors calculated using Chemicalize software to propose an easy-to-interpretation QSRR model. Considering the heterogeneous character of the data set, locally weighted least squares kernel regression belonging to similarity-based machine learning methods have been applied. The results showed that lipophilicity, charge, and maximum projection area determine molecule binding to phospholipids. Full validation of the obtained model based on OECD recommendations has been performed and the applicability domain was defined using the probability-oriented distance-based approach. The high values of predictive squared correlation coefficient (Q2), and small root mean square error of prediction (RMSEP), 0.812 and 6.739, respectively, confirmed that the obtained QSRR model is not well-fitted to the training data but also showed prediction power. Additionally, only 1.5% of molecules from the training set and 2.8% from the validation test are outside the applicability domain, confirming great predictive abilities.

Improving ADMET Prediction Accuracy for Candidate Drugs: Factors to Consider in QSPR Modeling Approaches

Quantitative Structure-Property Relationship (QSPR) employs mathematical and statistical methods to reveal quantitative correlations between the pharmacokinetics of compounds and their molecular structures, as well as their physical and chemical properties. QSPR models have been widely applied in the prediction of drug absorption, distribution, metabolism, excretion, and toxicity (ADMET). However, the accuracy of QSPR models for predicting drug ADMET properties still needs improvement. Therefore, this paper comprehensively reviews the tools employed in various stages of QSPR predictions for drug ADMET. It summarizes commonly used approaches to building QSPR models, systematically analyzing the advantages and limitations of each modeling method to ensure their judicious application. We provide an overview of recent advancements in the application of QSPR models for predicting drug ADMET properties. Furthermore, this review explores the inherent challenges in QSPR modeling while also proposing a range of considerations aimed at enhancing model prediction accuracy. The objective is to enhance the predictive capabilities of QSPR models in the field of drug development and provide valuable reference and guidance for researchers in this domain.

A flexible data-free framework for structure-based de novo drug design with reinforcement learning

Contemporary structure-based molecular generative methods have demonstrated their potential to model the geometric and energetic complementarity between ligands and receptors, thereby facilitating the design of molecules with favorable binding affinity and target specificity. Despite the introduction of deep generative models for molecular generation, the atom-wise generation paradigm that partially contradicts chemical intuition limits the validity and synthetic accessibility of the generated molecules. Additionally, the dependence of deep learning models on large-scale structural data has hindered their adaptability across different targets. To overcome these challenges, we present a novel search-based framework, 3D-MCTS, for structure-based de novo drug design. Distinct from prevailing atom-centric methods, 3D-MCTS employs a fragment-based molecular editing strategy. The fragments decomposed from small-molecule drugs are recombined under predefined retrosynthetic rules, offering improved drug-likeness and synthesizability, overcoming the inherent limitations of atom-based approaches. Leveraging multi-threaded parallel simulations combined with a real-time energy constraint-based pruning strategy, 3D-MCTS achieves remarkable efficiency. At a fixed computational cost, it outperforms other state-of-the-art (SOTA) methods by producing molecules with enhanced binding affinity. Furthermore, its fragment-based approach ensures the generation of more dependable binding conformations, exhibiting a success rate 43.6% higher than that of other SOTAs. This advantage becomes even more pronounced when handling targets that significantly deviate from the training dataset. 3D-MCTS is capable of achieving thirty times more hits with high binding affinity than traditional virtual screening methods, which demonstrates the superior ability of 3D-MCTS to explore chemical space. Moreover, the flexibility of our framework makes it easy to incorporate domain knowledge during the process, thereby enabling the generation of molecules with desirable pharmacophores and enhanced binding affinity. The adaptability of 3D-MCTS is further showcased in metalloprotein applications, highlighting its potential across various drug design scenarios.

Design, synthesis and anticancer evaluation of polymethoxy aurones as potential cell cycle inhibitors

Background

Cancer is the most fatal disease in humans and the aberrant activity of various cell cycle proteins results in uncontrolled tumor cell proliferation, thus, regulating the cell cycle is an attractive target in cancer therapy.

Objectives

Aurone is a naturally occurring active compound with a wide range of biological activities, of which 3, 4, 5-trimethoxyphenyl (TMP) is an important microtubule targeting pharmacophore. Based on the pharmacophore combination principle, we incorporate the TMP pharmacophore into the aurone structure and design a novel polymethoxy derivative that is expected to inhibit tumor cell proliferation through regulating the cell cycle.

Methods

By introducing different substituents on C-4' and C-3', a series of new 4, 5, 6-trimethoxy aurone derivatives have been designed and synthesized. DU145, MCF-7 and H1299 cell lines were selected to evaluate their anticancer activity. The compound with the best cytotoxicity was then selected and the anticancer mechanisms were investigated by network pharmacology, flow cytometry, Western blot, and cell heat transfer assay. ADMET prediction evaluated the draggability of aurone derivatives.

Results

Aurones 1b and 1c have selective anti-proliferative activity against DU145 cells. Among them, the compound 1c have better cytotoxicity against DU145. Compound 1c could bind the active cavity of CyclinB1/CDK1/CKS complex protein and induced G2/M phase arrest of DU145 cells by regulating the expression of CyclinB1 and p21. Compound 1c satisfies the Lipinski rule, is suitable for the absorption and metabolism index, and has a lower risk of cardiac toxicity.

Conclusions

Polymethoxy aurones 1c might function as a CyclinB1/CDK1 inhibitor that deserved to be further developed for the treatment of prostate cancer.

Recent Advances in Toxicity Prediction: Applications of Deep Graph Learning

The development of new drugs is time-consuming and expensive, and as such, accurately predicting the potential toxicity of a drug candidate is crucial in ensuring its safety and efficacy. Recently, deep graph learning has become prevalent in this field due to its computational power and cost efficiency. Many novel deep graph learning methods aid toxicity prediction and further prompt drug development. This review aims to connect fundamental knowledge with burgeoning deep graph learning methods. We first summarize the essential components of deep graph learning models for toxicity prediction, including molecular descriptors, molecular representations, evaluation metrics, validation methods, and data sets. Furthermore, based on various graph-related representations of molecules, we introduce several representative studies and methods for toxicity prediction from the perspective of GNN architectures and graph pretrained models. Compared to other types of models, deep graph models not only advance in higher accuracy and efficiency but also provide more intuitive insights, which is significant in the development of model interpretation and generalization ability. The graph pretrained models are emerging as they can extract prominent features from large-scale unlabeled molecular graph data and improve the performance of downstream toxicity prediction tasks. We hope this survey can serve as a handbook for individuals interested in exploring deep graph learning for toxicity prediction.

Explaining Blood-Brain Barrier Permeability of Small Molecules by Integrated Analysis of Different Transport Mechanisms

The blood-brain barrier (BBB) represents a major obstacle to delivering drugs to the central nervous system (CNS), resulting in the lack of effective treatment for many CNS diseases including brain cancer. To accelerate CNS drug development, computational prediction models could save the time and effort needed for experimental evaluation. Here, we studied BBB permeability focusing on active transport (influx and efflux) as well as passive diffusion using previously published and self-curated data sets. We created prediction models based on physicochemical properties, molecular substructures, or their combination to understand which mechanisms contribute to BBB permeability. Our results show that features that predicted passive diffusion over membranes overlap with features that explain endothelial permeation of approved CNS-active drugs. We also identified physical properties and molecular substructures that positively or negatively predicted BBB transport. These findings provide guidance toward identifying BBB-permeable compounds by optimally matching physicochemical and molecular properties to BBB transport mechanisms.

Evaluation of therapeutic potentials of selected phytochemicals against Nipah virus, a multi-dimensional in silico study

The current study attempted to evaluate the potential of fifty-three (53) natural compounds as Nipah virus attachment glycoprotein (NiV G) inhibitors through in silico molecular docking study. Pharmacophore alignment of the four (4) selected compounds (Naringin, Mulberrofuran B, Rutin and Quercetin 3-galactoside) through Principal Component Analysis (PCA) revealed that common pharmacophores, namely four H bond acceptors, one H bond donor and two aromatic groups were responsible for the residual interaction with the target protein. Out of these four compounds, Naringin was found to have the highest inhibitory potential ( - 9.19 kcal mol^-1) against the target protein NiV G, when compared to the control drug, Ribavirin ( - 6.95 kcal mol^-1). The molecular dynamic simulation revealed that Naringin could make a stable complex with the target protein in the near-native physiological condition. Finally, MM-PBSA (Molecular Mechanics-Poisson-Boltzmann Solvent-Accessible Surface Area) analysis in agreement with our molecular docking result, showed that Naringin ( - 218.664 kJ mol^-1) could strongly bind with the target protein NiV G than the control drug Ribavirin ( - 83.812 kJ mol^-1).

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03595-y.

Role of bacterial efflux pumps in antibiotic resistance, virulence, and strategies to discover novel efflux pump inhibitors

The problem of antibiotic resistance among pathogenic bacteria has reached a crisis level. The treatment options against infections caused by multiple drug-resistant bacteria are shrinking gradually. The current pace of the discovery of new antibacterial entities is lagging behind the rate of development of new resistance. Efflux pumps play a central role in making a bacterium resistant to multiple antibiotics due to their ability to expel a wide range of structurally diverse compounds. Besides providing an escape from antibacterial compounds, efflux pumps are also involved in bacterial stress response, virulence, biofilm formation, and altering host physiology. Efflux pumps are unique yet challenging targets for the discovery of novel efflux pump inhibitors (EPIs). EPIs could help rejuvenate our currently dried pipeline of antibacterial drug discovery. The current article highlights the recent developments in the field of efflux pumps, challenges faced during the development of EPIs and potential approaches for their development. Additionally, this review highlights the utility of resources such as natural products and machine learning to expand our EPIs arsenal using these latest technologies.

Evaluation of tea (Camellia sinensis L.) phytochemicals as multi-disease modulators, a multidimensional in silico strategy with the combinations of network pharmacology, pharmacophore analysis, statistics and molecular docking

Tea (Camellia sinensis L.) is considered as to be one of the most consumed beverages globally and a reservoir of phytochemicals with immense health benefits. Despite numerous advantages, tea compounds lack a robust multi-disease target study. In this work, we presented a unique in silico approach consisting of molecular docking, multivariate statistics, pharmacophore analysis, and network pharmacology approaches. Eight tea phytochemicals were identified through literature mining, namely gallic acid, catechin, epigallocatechin gallate, epicatechin, epicatechin gallate (ECG), quercetin, kaempferol, and ellagic acid, based on their richness in tea leaves. Further, exploration of databases revealed 30 target proteins related to the pharmacological properties of tea compounds and multiple associated diseases. Molecular docking experiment with eight tea compounds and all 30 proteins revealed that except gallic acid all other seven phytochemicals had potential inhibitory activities against these targets. The docking experiment was validated by comparing the binding affinities (Kcal mol^-1) of the compounds with known drug molecules for the respective proteins. Further, with the aid of the application of statistical tools (principal component analysis and clustering), we identified two major clusters of phytochemicals based on their chemical properties and docking scores (Kcal mol^-1). Pharmacophore analysis of these clusters revealed the functional descriptors of phytochemicals, related to the ligand-protein docking interactions. Tripartite network was constructed based on the docking scores, and it consisted of seven tea phytochemicals (gallic acid was excluded) targeting five proteins and ten associated diseases. Epicatechin gallate (ECG)-hepatocyte growth factor receptor (PDB id 1FYR) complex was found to be highest in docking performance (10 kcal mol^-1). Finally, molecular dynamic simulation showed that ECG-1FYR could make a stable complex in the near-native physiological condition.

ADMET profiling and molecular docking of potential antimicrobial peptides previously isolated from African catfish, Clarias gariepinus

Amidst rising cases of antimicrobial resistance, antimicrobial peptides (AMPs) are regarded as a promising alternative to traditional antibiotics. Even so, poor pharmacokinetic profiles of certain AMPs impede their utility necessitating, a careful assessment of potential AMPs' absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties during novel lead exploration. Accordingly, the present study utilized ADMET scores to profile seven previously isolated African catfish antimicrobial peptides (ACAPs). After profiling, the peptides were docked against approved bacterial protein targets to gain insight into their possible mode of action. Promising ACAPs were then chemically synthesized, and their antibacterial activity was validated in vitro utilizing the broth dilution method. All seven examined antimicrobial peptides passed the ADMET screening, with two (ACAP-IV and ACAP-V) exhibiting the best ADMET profile scores. The ACAP-V had a higher average binding energy (-8.47 kcal/mol) and average global energy (-70.78 kcal/mol) compared to ACAP-IV (-7.60 kcal/mol and -57.53 kcal/mol), with the potential to penetrate and disrupt bacterial cell membrane (PDB Id: 2w6d). Conversely, ACAP-IV peptide had higher antibacterial activity against E. coli and S. aureus (Minimum Inhibitory Concentration, 520.7 ± 104.3 μg/ml and 1666.7 ± 416.7 μg/ml, respectively) compared to ACAP-V. Collectively, the two antimicrobial peptides (ACAP-IV and ACAP-V) are potential novel leads for the food, cosmetic and pharmaceutical industries. Future research is recommended to optimize the expression of such peptides in biological systems for extended evaluation.

Structural Feature Analyzation Strategies toward Discovery of Orally Bioavailable PROTACs of Bruton's Tyrosine Kinase for the Treatment of Lymphoma

Bruton's tyrosine kinase proteolysis-targeting chimeras (BTK-PROTACs) have emerged as a promising approach to address the limitations of BTK inhibitors. However, conducting the rational discovery of orally bioavailable BTK-PROTACs presents significant challenges. In this study, dimensionality reduction analysis and model molecule validation were utilized to identify some key structural features for improving the oral absorption of BTK-PROTACs. The results were applied to optimize the newly discovered BTK-PROTACs B1 and B2. Compound C13 was discovered with improved oral bioavailability, high BTK degradation activity, and selectivity. It exhibited inhibitory effects against different hematologic cancer cells and attenuated the BTK-related signaling pathway. The oral administration of C13 effectively reduced BTK protein levels and suppressed tumor growth. This study led to the discovery of a new orally bioavailable BTK-PROTAC for the treatment of lymphoma, and we hope that the strategy will find wide utility.

Natural Products-Based Drug Design against SARS-CoV-2 Mpro 3CLpro

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has received global attention due to the serious threat it poses to public health. Since the outbreak in December 2019, millions of people have been affected and its rapid global spread has led to an upsurge in the search for treatment. To discover hit compounds that can be used alone or in combination with repositioned drugs, we first analyzed the pharmacokinetic and toxicological properties of natural products from Brazil's semiarid region. After, we analyzed the site prediction and druggability of the SARS-CoV-2 main protease (Mpro), followed by docking and molecular dynamics simulation. The best SARS-CoV-2 Mpro complexes revealed that other sites were accessed, confirming that our approach could be employed as a suitable starting protocol for ligand prioritization, reinforcing the importance of catalytic cysteine-histidine residues and providing new structural data that could increase the antiviral development mainly against SARS-CoV-2. Here, we selected 10 molecules that could be in vitro assayed in response to COVID-19. Two compounds (b01 and b02) suggest a better potential for interaction with SARS-CoV-2 Mpro and could be further studied.

Multitask CapsNet: An Imbalanced Data Deep Learning Method for Predicting Toxicants

Drug development has a high failure rate, with safety properties constituting a considerable challenge. To reduce risk, in silico tools, including various machine learning methods, have been applied for toxicity prediction. However, these approaches often confront a serious problem: the training data sets are usually biased (imbalanced positive and negative samples), which would result in model training difficulty and unsatisfactory prediction accuracy. Multitask networks obtained significantly better predictive accuracies than single-task methods, and capsule neural networks showed excellent performance in sparse data sets in previous studies. In this study, we developed a new multitask framework based on a capsule neural network (multitask CapsNet) to measure 12 different toxic effects simultaneously. We found that multitask CapsNet excelled in toxicity prediction and outperformed many other computational approaches using the multitask strategy. Only after training on biased data sets did multitask CapsNet achieve significantly improved prediction accuracy on the Tox21 Data Challenge, which gave the largest ratio of highest accuracy (8/12) among compared models. Our model gave a prediction accuracy of 96.6% for the target NR.PPAR.gamma, whose ratio of negative to positive samples was up to 36:1. These results suggested that multitask CapsNet could overcome the bias problems and would provide a novel, accurate, and efficient approach for predicting the toxicities of compounds.

ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties

Because undesirable pharmacokinetics and toxicity of candidate compounds are the main reasons for the failure of drug development, it has been widely recognized that absorption, distribution, metabolism, excretion and toxicity (ADMET) should be evaluated as early as possible. In silico ADMET evaluation models have been developed as an additional tool to assist medicinal chemists in the design and optimization of leads. Here, we announced the release of ADMETlab 2.0, a completely redesigned version of the widely used AMDETlab web server for the predictions of pharmacokinetics and toxicity properties of chemicals, of which the supported ADMET-related endpoints are approximately twice the number of the endpoints in the previous version, including 17 physicochemical properties, 13 medicinal chemistry properties, 23 ADME properties, 27 toxicity endpoints and 8 toxicophore rules (751 substructures). A multi-task graph attention framework was employed to develop the robust and accurate models in ADMETlab 2.0. The batch computation module was provided in response to numerous requests from users, and the representation of the results was further optimized. The ADMETlab 2.0 server is freely available, without registration, at https://admetmesh.scbdd.com/.

A comprehensive review of integrative pharmacology-based investigation: A paradigm shift in traditional Chinese medicine

Over the past decade, traditional Chinese medicine (TCM) has widely embraced systems biology and its various data integration approaches to promote its modernization. Thus, integrative pharmacology-based traditional Chinese medicine (TCMIP) was proposed as a paradigm shift in TCM. This review focuses on the presentation of this novel concept and the main research contents, methodologies and applications of TCMIP. First, TCMIP is an interdisciplinary science that can establish qualitative and quantitative pharmacokinetics-pharmacodynamics (PK-PD) correlations through the integration of knowledge from multiple disciplines and techniques and from different PK-PD processes in vivo. Then, the main research contents of TCMIP are introduced as follows: chemical and ADME/PK profiles of TCM formulas; confirming the three forms of active substances and the three action modes; establishing the qualitative PK-PD correlation; and building the quantitative PK-PD correlations, etc. After that, we summarize the existing data resources, computational models and experimental methods of TCMIP and highlight the urgent establishment of mathematical modeling and experimental methods. Finally, we further discuss the applications of TCMIP for the improvement of TCM quality control, clarification of the molecular mechanisms underlying the actions of TCMs and discovery of potential new drugs, especially TCM-related combination drug discovery.

Drivers of absolute systemic bioavailability after oral pulmonary inhalation in humans

There are few studies in humans dealing with the relationship between physico-chemical properties of drugs and their systemic bioavailability after administration via oral inhalation route (Fpulm). Getting further insight in the determinants of Fpulm after oral pulmonary inhalation could be of value for drugs considered for a systemic delivery as a result of poor oral bioavailability, as well as for drugs considered for a local delivery to anticipate their undesirable systemic effects. To better delineate the parameters influencing the systemic delivery after oral pulmonary inhalation in humans, we studied the influence of physico-chemical and permeability properties obtained in silico on the rate and extent of Fpulm in a series of 77 compounds with or without marketing approval for pulmonary delivery, and intended either for local or for systemic delivery. Principal component analysis (PCA) showed mainly that Fpulm was positively correlated with Papp and negatively correlated with %TPSA, without a significant influence of solubility and ionization fraction, and no apparent link with lipophilicity and drug size parameters. As a result of the small sample set, the performance of the different models as predictive of Fpulm were quite average with random forest algorithm displaying the best performance. As a whole, the different models captured between 50 and 60% of the variability with a prediction error of less than 20%. Tmax data suggested a significant positive influence of lipophilicity on absorption rate while charge apparently had no influence. A significant linear relationship between Cmax and dose (R² = "0.79) highlighted that Cmax was primarily dependent on dose and absorption rate and could be used to estimate Cmax in humans for new inhaled drugs.

Modeling and Simulation of Process Technology for Nanoparticulate Drug Formulations-A Particle Technology Perspective

Crystalline organic nanoparticles and their amorphous equivalents (ONP) have the potential to become a next-generation formulation technology for dissolution-rate limited biopharmaceutical classification system (BCS) class IIa molecules if the following requisites are met: (i) a quantitative understanding of the bioavailability enhancement benefit versus established formulation technologies and a reliable track record of successful case studies are available; (ii) efficient experimentation workflows with a minimum amount of active ingredient and a high degree of digitalization via, e.g., automation and computer-based experimentation planning are implemented; (iii) the scalability of the nanoparticle-based oral delivery formulation technology from the lab to manufacturing is ensured. Modeling and simulation approaches informed by the pharmaceutical material science paradigm can help to meet these requisites, especially if the entire value chain from formulation to oral delivery is covered. Any comprehensive digitalization of drug formulation requires combining pharmaceutical materials science with the adequate formulation and process technologies on the one hand and quantitative pharmacokinetics and drug administration dynamics in the human body on the other hand. Models for the technical realization of the drug production and the distribution of the pharmaceutical compound in the human body are coupled via the central objective, namely bioavailability. The underlying challenges can only be addressed by hierarchical approaches for property and process design. The tools for multiscale modeling of the here-considered particle processes (e.g., by coupled computational fluid dynamics, population balance models, Noyes–Whitney dissolution kinetics) and physiologically based absorption modeling are available. Significant advances are being made in enhancing the bioavailability of hydrophobic compounds by applying innovative solutions. As examples, the predictive modeling of anti-solvent precipitation is presented, and options for the model development of comminution processes are discussed.

Computational Drug Designing and Prediction Of Important Parameters Using in silico Methods- A Review

BACKGROUND

Computational or in silico studies are undertaken to assess the drug like properties of lead compounds. These studies help in fast prediction of relevant properties.

OBJECTIVE

Through this review, an effort is made to encapsulate some of the important parameters which should be met by a compound for it to be considered as a potential drug candidate along with an overview of automated softwares which can be used for making various predictions.

METHODS

Drug uptake, its absorption, evacuation and associated hazardous effects are important factors for consideration in drug designing and should be known in early stages of drug development. Several important physicochemical properties like molecular weight, polar surface area (PSA), molecular flexibility etc. have to be taken into consideration in drug designing. Toxicological assessment is another important aspect of drug discovery which predicts the safety and adverse effects of a drug.

RESULTS

Additionally, bioactivity scores of probable drug leads against various human receptors can also be predicted to evaluate the probability of them to act as a potential drug candidate. The in vivo biological targets of a molecule can also be efficiently predicted by molecular docking studies.

CONCLUSION

Some important software like iGEMDOCK, AutoDock, OSIRIS property explorer, Molinspiration, MetaPrint2D, admetSAR and their working methodology and principle of working have been summarized in this review.

Capsule Networks Showed Excellent Performance in the Classification of hERG Blockers/Nonblockers

Capsule networks (CapsNets), a new class of deep neural network architectures proposed recently by Hinton et al., have shown a great performance in many fields, particularly in image recognition and natural language processing. However, CapsNets have not yet been applied to drug discovery-related studies. As the first attempt, we in this investigation adopted CapsNets to develop classification models of hERG blockers/nonblockers; drugs with hERG blockade activity are thought to have a potential risk of cardiotoxicity. Two capsule network architectures were established: convolution-capsule network (Conv-CapsNet) and restricted Boltzmann machine-capsule networks (RBM-CapsNet), in which convolution and a restricted Boltzmann machine (RBM) were used as feature extractors, respectively. Two prediction models of hERG blockers/nonblockers were then developed by Conv-CapsNet and RBM-CapsNet with the Doddareddy's training set composed of 2,389 compounds. The established models showed excellent performance in an independent test set comprising 255 compounds, with prediction accuracies of 91.8 and 92.2% for Conv-CapsNet and RBM-CapsNet models, respectively. Various comparisons were also made between our models and those developed by other machine learning methods including deep belief network (DBN), convolutional neural network (CNN), multilayer perceptron (MLP), support vector machine (SVM), k-nearest neighbors (kNN), logistic regression (LR), and LightGBM, and with different training sets. All the results showed that the models by Conv-CapsNet and RBM-CapsNet are among the best classification models. Overall, the excellent performance of capsule networks achieved in this investigation highlights their potential in drug discovery-related studies.

“Drug-likeness” properties of natural compounds

Our previous work was focused on the fundamental physical and chemical concepts behind “drug-likeness” and “natural product (NP)-likeness”. Herein, we discuss further details on the concepts of “drug-likeness”, “lead-likeness” and “NP-likeness”. The discussion will first focus on NPs as drugs, then a discussion of previous studies in which the complexities of the scaffolds and chemical space of naturally occurring compounds have been compared with synthetic, semisynthetic compounds and the Food and Drug Administration-approved drugs. This is followed by guiding principles for designing “drug-like” natural product libraries for lead compound discovery purposes. In addition, we present a tool for measuring “NP-likeness” of compounds and a brief presentation of machine-learning approaches. A binary quantitative structure–activity relationship for classifying drugs from nondrugs and natural compounds from nonnatural ones is also described. While the studies add to the plethora of recently published works on the “drug-likeness” of NPs, it no doubt increases our understanding of the physicochemical properties that make NPs fall within the ranges associated with “drug-like” molecules.

In Silico Prediction of Drug-Induced Liver Injury Based on Ensemble Classifier Method

Drug-induced liver injury (DILI) is a major factor in the development of drugs and the safety of drugs. If the DILI cannot be effectively predicted during the development of the drug, it will cause the drug to be withdrawn from markets. Therefore, DILI is crucial at the early stages of drug research. This work presents a 2-class ensemble classifier model for predicting DILI, with 2D molecular descriptors and fingerprints on a dataset of 450 compounds. The purpose of our study is to investigate which are the key molecular fingerprints that may cause DILI risk, and then to obtain a reliable ensemble model to predict DILI risk with these key factors. Experimental results suggested that 8 molecular fingerprints are very critical for predicting DILI, and also obtained the best ratio of molecular fingerprints to molecular descriptors. The result of the 5-fold cross-validation of the ensemble vote classifier method obtain an accuracy of 77.25%, and the accuracy of the test set was 81.67%. This model could be used for drug-induced liver injury prediction.

TCM-ADMEpred: A novel strategy for poly-pharmacokinetics prediction of traditional Chinese medicine based on single constituent pharmacokinetics, structural similarity, and mathematical modeling

ETHNOPHARMACOLOGICAL RELEVANCE

Yuanhu Zhitong prescription (YZP) is a commonly used and relatively simple clinical herb preparation recorded in the China Pharmacopoeia. It contains Corydalis yanhusuo (Chinese name, Yanhusuo [YH]) and Angelica dahurica (Hoffm.) (Chinese name, Baizhi [BZ]), and has a long history of use in traditional Chinese medicine (TCM) for the treatment of stomach pain, hypochondriac pain, headache, and dysmenorrhea.

AIM OF THE STUDY

A TCM-ADMEpred method is developed for novel strategy for poly-pharmacokinetics prediction of TCM. To predict the pharmacokinetic characteristics of the main YZP constituents in rat plasma using in silico models, based on the theory that structurally similar constituents show similar pharmacokinetic properties. This approach may facilitate in silico prediction of the pharmacokinetics of TCM.

MATERIALS AND METHODS

A robust platform using ultra-performance liquid chromatography coupled with triple quadrupole electrospray tandem mass spectrometry (UPLC-ESI-MS/MS) was developed and validated for simultaneous determination of seven active YZP constituents in rat plasma. These seven compounds were divided into two structural classes, alkaloids and coumarins. The correlation between AUC profiles within a structural class was expressed as Γ⁺, and this variable was used to develop two novel in silico models to predict constituent AUC values. The pharmacokinetics of tetrahydropalmatine, tetrahydroberberine, and corydaline following YZP administration were predicted using the Γ⁺-values of α-allocryptopine observed following YH administration, while those of imperatorin and isoimperatorin following BZ administration were predicted using the Γ⁺-values of byakangelicin observed following YZP administration.

RESULTS

The UPLC-ESI-MS/MS method was successfully used to evaluate pharmacokinetic parameters after oral YZP, YH, or BZ administration. Our findings showed that co-administration of YH and BZ increased the AUC of four alkaloid constituents and reduced the AUC of three coumarin constituents, which might provide a scientific rationale for co-administering these herbs clinically as a YZP preparation, thus increasing their efficacy and reducing toxicity. The AUC values of imperatorin and isoimperatorin were predicted 3 h after oral BZ administration, with the bias ratios between the theoretical values and the observed experimental values ranging from 0.61% to 11.4%, and average bias ratios of 5.8% and 8.0%, respectively. The AUC values of tetrahydropalmatine, tetrahydroberberine, and corydaline were predicted 3 h after oral YZP administration, with bias ratios ranging from 3.7% to 46.4%, and average bias ratios of 23.8%, 15.4%, and 25.8%, respectively.

CONCLUSION

The UPLC-ESI-MS/MS method was successfully applied to pharmacokinetic evaluations after oral administration of YZP, YH, and BZ to rats. The Γ⁺ variable was used to express the correlation between the AUC profiles of structurally similar compounds. This facilitated the development of an in silico model that was used to predict the AUC of three alkaloids in YZP and of two coumarins in BZ. Calculation of the bias ratios between the predicted and experimental values suggested that this in silico model provided a viable approach for the prediction of TCM pharmacokinetics.

The mechanisms of pharmacokinetic food-drug interactions - A perspective from the UNGAP group

The simultaneous intake of food and drugs can have a strong impact on drug release, absorption, distribution, metabolism and/or elimination and consequently, on the efficacy and safety of pharmacotherapy. As such, food-drug interactions are one of the main challenges in oral drug administration. Whereas pharmacokinetic (PK) food-drug interactions can have a variety of causes, pharmacodynamic (PD) food-drug interactions occur due to specific pharmacological interactions between a drug and particular drinks or food. In recent years, extensive efforts were made to elucidate the mechanisms that drive pharmacokinetic food-drug interactions. Their occurrence depends mainly on the properties of the drug substance, the formulation and a multitude of physiological factors. Every intake of food or drink changes the physiological conditions in the human gastrointestinal tract. Therefore, a precise understanding of how different foods and drinks affect the processes of drug absorption, distribution, metabolism and/or elimination as well as formulation performance is important in order to be able to predict and avoid such interactions. Furthermore, it must be considered that beverages such as milk, grapefruit juice and alcohol can also lead to specific food-drug interactions. In this regard, the growing use of food supplements and functional food requires urgent attention in oral pharmacotherapy. Recently, a new consortium in Understanding Gastrointestinal Absorption-related Processes (UNGAP) was established through COST, a funding organisation of the European Union supporting translational research across Europe. In this review of the UNGAP Working group "Food-Drug Interface", the different mechanisms that can lead to pharmacokinetic food-drug interactions are discussed and summarised from different expert perspectives.

An Integrated Transfer Learning and Multitask Learning Approach for Pharmacokinetic Parameter Prediction

BACKGROUND

Pharmacokinetic evaluation is one of the key processes in drug discovery and development. However, current absorption, distribution, metabolism, and excretion prediction models still have limited accuracy.

AIM

This study aims to construct an integrated transfer learning and multitask learning approach for developing quantitative structure-activity relationship models to predict four human pharmacokinetic parameters.

METHODS

A pharmacokinetic data set included 1104 U.S. FDA approved small molecule drugs. The data set included four human pharmacokinetic parameter subsets (oral bioavailability, plasma protein binding rate, apparent volume of distribution at steady-state, and elimination half-life). The pretrained model was trained on over 30 million bioactivity data entries. An integrated transfer learning and multitask learning approach was established to enhance the model generalization.

RESULTS

The pharmacokinetic data set was split into three parts (60:20:20) for training, validation, and testing by the improved maximum dissimilarity algorithm with the representative initial set selection algorithm and the weighted distance function. The multitask learning techniques enhanced the model predictive ability. The integrated transfer learning and multitask learning model demonstrated the best accuracies, because deep neural networks have the general feature extraction ability; transfer learning and multitask learning improve the model generalization.

CONCLUSIONS

The integrated transfer learning and multitask learning approach with the improved data set splitting algorithm was first introduced to predict the pharmacokinetic parameters. This method can be further employed in drug discovery and development.

High throughput screening against pantothenate synthetase identifies amide inhibitors against Mycobacterium tuberculosis and Staphylococcus aureus

Abstract

Pantothenate is a crucial enzyme for the synthesis of coenzyme A and acyl carrier protein in Mycobacterium tuberculosis and Staphylococcus aureus. It is indispensable for the growth and survival of these bacteria. Amides analogs are designed and have been used as inhibitors of pantothenate synthetase. Molecular docking approach has been used to design and predict the drug activity of molecule to the specific disease. In this work, more than hundred amides have been screened by Discovery Studio molecular docking programme to search best suitable molecule for the treatment of Mycobacterium tuberculosis. Pharmacophore generation has been done to recognize the binding modes of inhibitors in the receptor active site. To observe the stability and flexibility of inhibitors molecular dynamics (MD) simulation has been done; Lipinski's rule of five protocols is followed to screen drug likeness and ADMET (absorption, distribution, metabolism, excretion and toxicity) filtration is also used to value toxicity. DFT computation of optimized geometry and derivation of MOs has been used to correlate the drug likeness. The small difference in energy between HOMO and LUMO may help to activate the drug in the protein environment quickly. 2-Hydroxy-5-[(E)-2-{4-[(prop-2-enamido)sulfonyl]phenyl}diazen-1-yl]benzoic acid (M1) shows best theoretical efficiency against Mycobacterium tuberculosis (MTB) pantothenate synthetase and so does 2-hydroxy-5-[(E)-2-{4-[(2-phenylacetamido)sulfonyl]phenyl}diazen-1-yl]benzoic acid (M2) against Staphylococcus aureus pantothenate synthetase. These compounds also bind to Adenine-Thymine region of tuberculosis DNA.

Graphical abstract

Computational modeling of human oral bioavailability: what will be next?

INTRODUCTION

The oral route is the most convenient way of administrating drugs. Therefore, accurate determination of oral bioavailability is paramount during drug discovery and development. Quantitative structure-property relationship (QSPR), rule-of-thumb (RoT) and physiologically based-pharmacokinetic (PBPK) approaches are promising alternatives to the early oral bioavailability prediction. Areas covered: The authors give insight into the factors affecting bioavailability, the fundamental theoretical framework and the practical aspects of computational methods for predicting this property. They also give their perspectives on future computational models for estimating oral bioavailability. Expert opinion: Oral bioavailability is a multi-factorial pharmacokinetic property with its accurate prediction challenging. For RoT and QSPR modeling, the reliability of datasets, the significance of molecular descriptor families and the diversity of chemometric tools used are important factors that define model predictability and interpretability. Likewise, for PBPK modeling the integrity of the pharmacokinetic data, the number of input parameters, the complexity of statistical analysis and the software packages used are relevant factors in bioavailability prediction. Although these approaches have been utilized independently, the tendency to use hybrid QSPR-PBPK approaches together with the exploration of ensemble and deep-learning systems for QSPR modeling of oral bioavailability has opened new avenues for development promising tools for oral bioavailability prediction.

Sulfonamide derivatives as Mycobacterium tuberculosis inhibitors: in silico approach

Both DHPS (dihydropteroate synthase) and DHFR (dihydrofolate reductase) play important physiological roles in the survivability of Mycobacterium tuberculosis (MTB). Sulfonamides are the potent drugs to monitor growth and proliferation of MTBs by inhibiting the activity of DHPS and DHFR which could explain the mechanism of action of these molecules. In this work, 102 heterocyclic sulfonamides (HSF) have been screened by discovery studio molecular docking programme to search the best suitable molecule for the treatment of MTBs. Lipinski's rule of five protocols is followed to screen drug likeness of these molecules and ADMET (absorption, distribution, metabolism, excretion and toxicity) filtration has been used to value their toxicity. Only fourteen molecules are found to obey the Lipinski's rule and able to cross the ADMET filter. A small difference between HOMO and LUMO energy signifies the electronic excitation energy which is essential to calculate molecular reactivity and stability of the best docked compound and easy activation of drug in the protein environment. Both 4-amino-N-(6-hydroxypyridin-2-yl)benzenesulfonamide (M1) and 4-amino-N-(9H-carbazol-2-yl)benzenesulfonamide (M2) show the best theoretical efficiency with DHPS and DHFR, respectively. These compounds are also found to bind to the adenine-thymine region of tuberculosis DNA.

The development and application of in silico models for drug induced liver injury

Drug-induced liver injury (DILI), caused by drugs, herbal agents or nutritional supplements, is a major issue for patients and the pharmaceutical industry. It has been a leading cause of clinical trials failure and withdrawal of FDA approval. In this research, we focused on in silico estimation of chemical DILI potential on humans based on structurally diverse organic chemicals. We developed a series of binary classification models using five different machine learning methods and eight different feature reduction methods. The model, developed with the support vector machine (SVM) and the MACCS fingerprint, performed best both on the test set and external validation. It achieved a prediction accuracy of 80.39% on the test set and 82.78% on external validation. We made this model available at http://opensource.vslead.com/. The user can freely predict the DILI potential of molecules. Furthermore, we analyzed the difference of distributions of 12 key physical-chemical properties between DILI-positive and DILI-negative compounds and 20 privileged substructures responsible for DILI were identified from the Klekota-Roth fingerprint. Moreover, since traditional Chinese medicine (TCM)-induced liver injury is also one of the major concerns among the toxic effects, we evaluated the DILI potential of TCM ingredients using the MACCS_SVM model developed in this study. We hope the model and privileged substructures could be useful complementary tools for chemical DILI evaluation.

Insights into the Molecular Basis of the Acute Contact Toxicity of Diverse Organic Chemicals in the Honey Bee

Use of chemical pollutants, including pesticides and other industrial chemicals, has resulted in significant risks to the whole ecosystem. Therefore, ecological risk assessment of chemicals is vital and necessary. Since the honey bee (Apis mellifera) is probably among the most exposed species to the polluting chemicals, we focused on the in silico estimation of honey bee toxicity (HBT) of chemicals and the analysis of the relevance of chemical HBT and several key physical-chemical properties and structural characteristics. A total of 40 classification models were developed by combination of five machine learning methods along with seven kinds of fingerprints and a set of molecular descriptors. After 5-fold cross validation and external validation, several models showed good predictive power. The relevance of 12 key physical-chemical properties and chemical HBT was also investigated. Five properties, including AlogP, logD, molecular weight (MW), molecular surface area (MSA), and the number of rotatable bonds (nRTB), indicated positive correlation coefficients with HBT, while molecular solubility (logS) and the number of hydrogen bond donors (nHBD) indicated negative correlation coefficients. Finally, seven privileged substructures responsible for chemical HBT were identified from KRFP and SubFP fingerprints. The results of this study should provide critical information and useful tools for chemical HBT estimation in environmental risk assessment.

Development of novel prediction model for drug-induced mitochondrial toxicity by using naïve Bayes classifier method

Mitochondrial dysfunction has been considered as an important contributing factor in the etiology of drug-induced organ toxicity, and even plays an important role in the pathogenesis of some diseases. The objective of this investigation was to develop a novel prediction model of drug-induced mitochondrial toxicity by using a naïve Bayes classifier. For comparison, the recursive partitioning classifier prediction model was also constructed. Among these methods, the prediction performance of naïve Bayes classifier established here showed best, which yielded average overall prediction accuracies for the internal 5-fold cross validation of the training set and external test set were 95 ± 0.6% and 81 ± 1.1%, respectively. In addition, four important molecular descriptors and some representative substructures of toxicants produced by ECFP_6 fingerprints were identified. We hope the established naïve Bayes prediction model can be employed for the mitochondrial toxicity assessment, and these obtained important information of mitochondrial toxicants can provide guidance for medicinal chemists working in drug discovery and lead optimization.

ADMET Evaluation in Drug Discovery. Part 17: Development of Quantitative and Qualitative Prediction Models for Chemical-Induced Respiratory Toxicity

As a dangerous end point, respiratory toxicity can cause serious adverse health effects and even death. Meanwhile, it is a common and traditional issue in occupational and environmental protection. Pharmaceutical and chemical industries have a strong urge to develop precise and convenient computational tools to evaluate the respiratory toxicity of compounds as early as possible. Most of the reported theoretical models were developed based on the respiratory toxicity data sets with one single symptom, such as respiratory sensitization, and therefore these models may not afford reliable predictions for toxic compounds with other respiratory symptoms, such as pneumonia or rhinitis. Here, based on a diverse data set of mouse intraperitoneal respiratory toxicity characterized by multiple symptoms, a number of quantitative and qualitative predictions models with high reliability were developed by machine learning approaches. First, a four-tier dimension reduction strategy was employed to find an optimal set of 20 molecular descriptors for model building. Then, six machine learning approaches were used to develop the prediction models, including relevance vector machine (RVM), support vector machine (SVM), regularized random forest (RRF), extreme gradient boosting (XGBoost), naïve Bayes (NB), and linear discriminant analysis (LDA). Among all of the models, the SVM regression model shows the most accurate quantitative predictions for the test set (q²_ext = 0.707), and the XGBoost classification model achieves the most accurate qualitative predictions for the test set (MCC of 0.644, AUC of 0.893, and global accuracy of 82.62%). The application domains were analyzed, and all of the tested compounds fall within the application domain coverage. We also examined the structural features of the compounds and important fragments with large prediction errors. In conclusion, the SVM regression model and the XGBoost classification model can be employed as accurate prediction tools for respiratory toxicity.

The Potential of Secondary Metabolites from Plants as Drugs or Leads against Protozoan Neglected Diseases-Part III: In-Silico Molecular Docking Investigations

Malaria, leishmaniasis, Chagas disease, and human African trypanosomiasis continue to cause considerable suffering and death in developing countries. Current treatment options for these parasitic protozoal diseases generally have severe side effects, may be ineffective or unavailable, and resistance is emerging. There is a constant need to discover new chemotherapeutic agents for these parasitic infections, and natural products continue to serve as a potential source. This review presents molecular docking studies of potential phytochemicals that target key protein targets in Leishmania spp., Trypanosoma spp., and Plasmodium spp.

Novel naïve Bayes classification models for predicting the carcinogenicity of chemicals

The carcinogenicity prediction has become a significant issue for the pharmaceutical industry. The purpose of this investigation was to develop a novel prediction model of carcinogenicity of chemicals by using a naïve Bayes classifier. The established model was validated by the internal 5-fold cross validation and external test set. The naïve Bayes classifier gave an average overall prediction accuracy of 90 ± 0.8% for the training set and 68 ± 1.9% for the external test set. Moreover, five simple molecular descriptors (e.g., AlogP, Molecular weight (M_W), No. of H donors, Apol and Wiener) considered as important for the carcinogenicity of chemicals were identified, and some substructures related to the carcinogenicity were achieved. Thus, we hope the established naïve Bayes prediction model could be applied to filter early-stage molecules for this potential carcinogenicity adverse effect; and the identified five simple molecular descriptors and substructures of carcinogens would give a better understanding of the carcinogenicity of chemicals, and further provide guidance for medicinal chemists in the design of new candidate drugs and lead optimization, ultimately reducing the attrition rate in later stages of drug development.

ADMET Evaluation in Drug Discovery. 16. Predicting hERG Blockers by Combining Multiple Pharmacophores and Machine Learning Approaches

Blockade of human ether-à-go-go related gene (hERG) channel by compounds may lead to drug-induced QT prolongation, arrhythmia, and Torsades de Pointes (TdP), and therefore reliable prediction of hERG liability in the early stages of drug design is quite important to reduce the risk of cardiotoxicity-related attritions in the later development stages. In this study, pharmacophore modeling and machine learning approaches were combined to construct classification models to distinguish hERG active from inactive compounds based on a diverse data set. First, an optimal ensemble of pharmacophore hypotheses that had good capability to differentiate hERG active from inactive compounds was identified by the recursive partitioning (RP) approach. Then, the naive Bayesian classification (NBC) and support vector machine (SVM) approaches were employed to construct classification models by integrating multiple important pharmacophore hypotheses. The integrated classification models showed improved predictive capability over any single pharmacophore hypothesis, suggesting that the broad binding polyspecificity of hERG can only be well characterized by multiple pharmacophores. The best SVM model achieved the prediction accuracies of 84.7% for the training set and 82.1% for the external test set. Notably, the accuracies for the hERG blockers and nonblockers in the test set reached 83.6% and 78.2%, respectively. Analysis of significant pharmacophores helps to understand the multimechanisms of action of hERG blockers. We believe that the combination of pharmacophore modeling and SVM is a powerful strategy to develop reliable theoretical models for the prediction of potential hERG liability.

ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling

Background

Determination of acute toxicity, expressed as median lethal dose (LD₅₀), is one of the most important steps in drug discovery pipeline. Because in vivo assays for oral acute toxicity in mammals are time-consuming and costly, there is thus an urgent need to develop in silico prediction models of oral acute toxicity.

Results

In this study, based on a comprehensive data set containing 7314 diverse chemicals with rat oral LD₅₀ values, relevance vector machine (RVM) technique was employed to build the regression models for the prediction of oral acute toxicity in rate, which were compared with those built using other six machine learning approaches, including k-nearest-neighbor regression, random forest (RF), support vector machine, local approximate Gaussian process, multilayer perceptron ensemble, and eXtreme gradient boosting. A subset of the original molecular descriptors and structural fingerprints (PubChem or SubFP) was chosen by the Chi squared statistics. The prediction capabilities of individual QSAR models, measured by q_ext² for the test set containing 2376 molecules, ranged from 0.572 to 0.659.

Conclusion

Considering the overall prediction accuracy for the test set, RVM with Laplacian kernel and RF were recommended to build in silico models with better predictivity for rat oral acute toxicity. By combining the predictions from individual models, four consensus models were developed, yielding better prediction capabilities for the test set (q_ext² = 0.669–0.689). Finally, some essential descriptors and substructures relevant to oral acute toxicity were identified and analyzed, and they may be served as property or substructure alerts to avoid toxicity. We believe that the best consensus model with high prediction accuracy can be used as a reliable virtual screening tool to filter out compounds with high rat oral acute toxicity. Graphical abstract

Chemoinformatics: Achievements and Challenges, a Personal View

Chemoinformatics provides computer methods for learning from chemical data and for modeling tasks a chemist is facing. The field has evolved in the past 50 years and has substantially shaped how chemical research is performed by providing access to chemical information on a scale unattainable by traditional methods. Many physical, chemical and biological data have been predicted from structural data. For the early phases of drug design, methods have been developed that are used in all major pharmaceutical companies. However, all domains of chemistry can benefit from chemoinformatics methods; many areas that are not yet well developed, but could substantially gain from the use of chemoinformatics methods. The quality of data is of crucial importance for successful results. Computer-assisted structure elucidation and computer-assisted synthesis design have been attempted in the early years of chemoinformatics. Because of the importance of these fields to the chemist, new approaches should be made with better hardware and software techniques. Society's concern about the impact of chemicals on human health and the environment could be met by the development of methods for toxicity prediction and risk assessment. In conjunction with bioinformatics, our understanding of the events in living organisms could be deepened and, thus, novel strategies for curing diseases developed. With so many challenging tasks awaiting solutions, the future is bright for chemoinformatics.

In silico prediction of hERG potassium channel blockage by chemical category approaches

The human ether-a-go-go related gene (hERG) plays an important role in cardiac action potential. It encodes an ion channel protein named Kv11.1, which is related to long QT syndrome and may cause avoidable sudden cardiac death. Therefore, it is important to assess the hERG channel blockage of lead compounds in an early drug discovery process. In this study, we collected a large data set containing 1163 diverse compounds with IC₅₀ values determined by the patch clamp method on mammalian cell lines. The whole data set was divided into 80% as the training set and 20% as the test set. Then, five machine learning methods were applied to build a series of binary classification models based on 13 molecular descriptors, five fingerprints and molecular descriptors combining fingerprints at four IC₅₀ thresholds to discriminate hERG blockers from nonblockers, respectively. Models built by molecular descriptors combining fingerprints were validated by using an external validation set containing 407 compounds collected from the hERGCentral database. The performance indicated that the model built by molecular descriptors combining fingerprints yielded the best results and each threshold had its best suitable method, which means that hERG blockage assessment might depend on threshold values. Meanwhile, kNN and SVM methods were better than the others for model building. Furthermore, six privileged substructures were identified using information gain and frequency analysis methods, which could be regarded as structural alerts of cardiac toxicity mediated by hERG channel blockage.

Identification of key active constituents of Buchang Naoxintong capsules with therapeutic effects against ischemic stroke by using an integrative pharmacology-based approach

Integrative pharmacology has been used to identify the key active constituents (KACs) of Buchang Naoxintong capsules (BNCs), a traditional Chinese medical preparation.

Advances in computationally modeling human oral bioavailability

Although significant progress has been made in experimental high throughput screening (HTS) of ADME (absorption, distribution, metabolism, excretion) and pharmacokinetic properties, the ADME and Toxicity (ADME-Tox) in silico modeling is still indispensable in drug discovery as it can guide us to wisely select drug candidates prior to expensive ADME screenings and clinical trials. Compared to other ADME-Tox properties, human oral bioavailability (HOBA) is particularly important but extremely difficult to predict. In this paper, the advances in human oral bioavailability modeling will be reviewed. Moreover, our deep insight on how to construct more accurate and reliable HOBA QSAR and classification models will also discussed.

Ensuring confidence in predictions: A scheme to assess the scientific validity of in silico models

The use of in silico tools within the drug development process to predict a wide range of properties including absorption, distribution, metabolism, elimination and toxicity has become increasingly important due to changes in legislation and both ethical and economic drivers to reduce animal testing. Whilst in silico tools have been used for decades there remains reluctance to accept predictions based on these methods particularly in regulatory settings. This apprehension arises in part due to lack of confidence in the reliability, robustness and applicability of the models. To address this issue we propose a scheme for the verification of in silico models that enables end users and modellers to assess the scientific validity of models in accordance with the principles of good computer modelling practice. We report here the implementation of the scheme within the Innovative Medicines Initiative project "eTOX" (electronic toxicity) and its application to the in silico models developed within the frame of this project.