An Aggregation Advisor for Ligand Discovery

Boosting the Accuracy and Chemical Space Coverage of the Detection of Small Colloidal Aggregating Molecules Using the BAD Molecule Filter

The ability to conduct effective high throughput screening (HTS) campaigns in drug discovery is often hampered by the detection of false positives in these assays due to small colloidally aggregating molecules (SCAMs). SCAMs can produce artifactual hits in HTS by nonspecific inhibition of the protein target. In this work, we present a new computational prediction tool for detecting SCAMs based on their 2D chemical structure. The tool, called the boosted aggregation detection (BAD) molecule filter, employs decision tree ensemble methods, namely, the CatBoost classifier and the light gradient-boosting machine, to significantly improve the detection of SCAMs. In developing the filter, we explore models trained on individual data sets, a consensus approach using these models, and, third, a merged data set approach, each tailored for specific drug discovery needs. The individual data set method emerged as most effective, achieving 93% sensitivity and 90% specificity, outperforming existing state-of-the-art models by 20 and 5%, respectively. The consensus models offer broader chemical space coverage, exceeding 90% for all testing sets. This feature is an important aspect particularly for early stage medicinal chemistry projects, and provides information on applicability domain. Meanwhile, the merged data set models demonstrated robust performance, with a notable sensitivity of 79% in the comprehensive 10-fold cross-validation test set. A SHAP analysis of model features indicates the importance of hydrophobicity and molecular complexity as primary factors influencing the aggregation propensity. The BAD molecule filter is readily accessible for the public usage on https://molmodlab-aau.com/Tools.html. This filter provides a new, more robust tool for aggregate prediction in the early stages of drug discovery to optimize hit rates and reduce associated testing and validation overheads.

ChemFH: an integrated tool for screening frequent false positives in chemical biology and drug discovery

High-throughput screening rapidly tests an extensive array of chemical compounds to identify hit compounds for specific biological targets in drug discovery. However, false-positive results disrupt hit compound screening, leading to wastage of time and resources. To address this, we propose ChemFH, an integrated online platform facilitating rapid virtual evaluation of potential false positives, including colloidal aggregators, spectroscopic interference compounds, firefly luciferase inhibitors, chemical reactive compounds, promiscuous compounds, and other assay interferences. By leveraging a dataset containing 823 391 compounds, we constructed high-quality prediction models using multi-task directed message-passing network (DMPNN) architectures combining uncertainty estimation, yielding an average AUC value of 0.91. Furthermore, ChemFH incorporated 1441 representative alert substructures derived from the collected data and ten commonly used frequent hitter screening rules. ChemFH was validated with an external set of 75 compounds. Subsequently, the virtual screening capability of ChemFH was successfully confirmed through its application to five virtual screening libraries. Furthermore, ChemFH underwent additional validation on two natural products and FDA-approved drugs, yielding reliable and accurate results. ChemFH is a comprehensive, reliable, and computationally efficient screening pipeline that facilitates the identification of true positive results in assays, contributing to enhanced efficiency and success rates in drug discovery. ChemFH is freely available via https://chemfh.scbdd.com/.

Predictions of Colloidal Molecular Aggregation Using AI/ML Models

To facilitate the triage of hits from small molecule screens, we have used various AI/ML techniques and experimentally observed data sets to build models aimed at predicting colloidal aggregation of small organic molecules in aqueous solution. We have found that Naïve Bayesian and deep neural networks outperform logistic regression, recursive partitioning tree, support vector machine, and random forest techniques by having the lowest balanced error rate (BER) for the test set. Derived predictive classification models consistently and successfully discriminated aggregator molecules from nonaggregator hits. An analysis of molecular descriptors in favor of colloidal aggregation confirms previous observations (hydrophobicity, molecular weight, and solubility) in addition to undescribed molecular descriptors such as the fraction of sp3 carbon atoms (Fsp3), and electrotopological state of hydroxyl groups (ES_Sum_sOH). Naïve Bayesian modeling and scaffold tree analysis have revealed chemical features/scaffolds contributing the most to colloidal aggregation and nonaggregation, respectively. These results highlight the importance of scaffolds with high Fsp3 values in promoting nonaggregation. Matched molecular pair analysis (MMPA) has also deciphered context-dependent substitutions, which can be used to design nonaggregator molecules. We found that most matched molecular pairs have a neutral effect on aggregation propensity. We have prospectively applied our predictive models to assist in chemical library triage for optimal plate selection diversity and purchase for high throughput screening (HTS) in drug discovery projects.

Colloidal Aggregation Confounds Cell-Based Covid-19 Antiviral Screens

Colloidal aggregation is one of the largest contributors to false positives in early drug discovery. Here, we consider aggregation's role in cell-based infectivity assays in Covid-19 drug repurposing. We investigated the potential aggregation of 41 drug candidates reported as SARs-CoV-2 entry inhibitors. Of these, 17 formed colloidal particles by dynamic light scattering and exhibited detergent-dependent enzyme inhibition. To evaluate the impact of aggregation on antiviral efficacy in cells, we presaturated the colloidal drug suspensions with BSA or spun them down by centrifugation and measured the effects on spike pseudovirus infectivity. Antiviral potencies diminished by at least 10-fold following both BSA and centrifugation treatments, supporting a colloid-based mechanism. Aggregates induced puncta of the labeled spike protein in fluorescence microscopy, consistent with sequestration of the protein on the colloidal particles. These observations suggest that colloidal aggregation is common among cell-based antiviral drug repurposing and offers rapid counter-screens to detect and eliminate these artifacts.

Develop Targeted Protein Drug Carriers through a High-Throughput Screening Platform and Rational Design

Protein-based drugs offer advantages, such as high specificity, low toxicity, and minimal side effects compared to small molecule drugs. However, delivery of proteins to target tissues or cells remains challenging due to the instability, diverse structures, charges, and molecular weights of proteins. Polymers have emerged as a leading choice for designing effective protein delivery systems, but identifying a suitable polymer for a given protein is complicated by the complexity of both proteins and polymers. To address this challenge, a fluorescence-based high-throughput screening platform called ProMatch to efficiently collect data on protein-polymer interactions, followed by in vivo and in vitro experiments with rational design is developed. Using this approach to streamline polymer selection for targeted protein delivery, candidate polymers from commercially available options are identified and a polyhexamethylene biguanide (PHMB)-based system for delivering proteins to white adipose tissue as a treatment for obesity is developed. A branched polyethylenimine (bPEI)-based system for neuron-specific protein delivery to stimulate optic nerve regeneration is also developed. The high-throughput screening methodology expedites identification of promising polymer candidates for tissue-specific protein delivery systems, thereby providing a platform to develop innovative protein-based therapeutics.

Systematic generation and analysis of counterfactuals for compound activity predictions using multi-task models

Most machine learning (ML) methods produce predictions that are hard or impossible to understand. The black box nature of predictive models obscures potential learning bias and makes it difficult to recognize and trace problems. Moreover, the inability to rationalize model decisions causes reluctance to accept predictions for experimental design. For ML, limited trust in predictions presents a substantial problem and continues to limit its impact in interdisciplinary research, including early-phase drug discovery. As a desirable remedy, approaches from explainable artificial intelligence (XAI) are increasingly applied to shed light on the ML black box and help to rationalize predictions. Among these is the concept of counterfactuals (CFs), which are best understood as test cases with small modifications yielding opposing prediction outcomes (such as different class labels in object classification). For ML applications in medicinal chemistry, for example, compound activity predictions, CFs are particularly intuitive because these hypothetical molecules enable immediate comparisons with actual test compounds that do not require expert ML knowledge and are accessible to practicing chemists. Such comparisons often reveal structural moieties in compounds that determine their predictions and can be further investigated. Herein, we adapt and extend a recently introduced concept for the systematic generation of molecular CFs to multi-task predictions of different classes of protein kinase inhibitors, analyze CFs in detail, rationalize the origins of CF formation in multi-task modeling, and present exemplary explanations of predictions.

Tackling assay interference associated with small molecules

Biochemical and cell-based assays are essential to discovering and optimizing efficacious and safe drugs, agrochemicals and cosmetics. However, false assay readouts stemming from colloidal aggregation, chemical reactivity, chelation, light signal attenuation and emission, membrane disruption, and other interference mechanisms remain a considerable challenge in screening synthetic compounds and natural products. To address assay interference, a range of powerful experimental approaches are available and in silico methods are now gaining traction. This Review begins with an overview of the scope and limitations of experimental approaches for tackling assay interference. It then focuses on theoretical methods, discusses strategies for their integration with experimental approaches, and provides recommendations for best practices. The Review closes with a summary of the critical facts and an outlook on potential future developments.

Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery

Efficient prioritization of bioactive compounds from high throughput screening campaigns is a fundamental challenge for accelerating drug development efforts. In this study, we present the first data-driven approach to simultaneously detect assay interferents and prioritize true bioactive compounds. By analyzing the learning dynamics during training of a gradient boosting model on noisy high throughput screening data using a novel formulation of sample influence, we are able to distinguish between compounds exhibiting the desired biological response and those producing assay artifacts. Therefore, our method enables false positive and true positive detection without relying on prior screens or assay interference mechanisms, making it applicable to any high throughput screening campaign. We demonstrate that our approach consistently excludes assay interferents with different mechanisms and prioritizes biologically relevant compounds more efficiently than all tested baselines, including a retrospective case study simulating its use in a real drug discovery campaign. Finally, our tool is extremely computationally efficient, requiring less than 30 s per assay on low-resource hardware. As such, our findings show that our method is an ideal addition to existing false positive detection tools and can be used to guide further pharmacological optimization after high throughput screening campaigns.

Molecular dynamics simulations as a guide for modulating small molecule aggregation

Small colloidally aggregating molecules (SCAMs) can be problematic for biological assays in drug discovery campaigns. However, the self-associating properties of SCAMs have potential applications in drug delivery and analytical biochemistry. Consequently, the ability to predict the aggregation propensity of a small organic molecule is of considerable interest. Chemoinformatics-based filters such as ChemAGG and Aggregator Advisor offer rapid assessment but are limited by the assay quality and structural diversity of their training set data. Complementary to these tools, we explore here the ability of molecular dynamics (MD) simulations as a physics-based method capable of predicting the aggregation propensity of diverse chemical structures. For a set of 32 molecules, using simulations of 100 ns in explicit solvent, we find a success rate of 97% (one molecule misclassified) as opposed to 75% by Aggregator Advisor and 72% by ChemAGG. These short timescale MD simulations are representative of longer microsecond trajectories and yield an informative spectrum of aggregation propensities across the set of solutes, capturing the dynamic behaviour of weakly aggregating compounds. Implicit solvent simulations using the generalized Born model were less successful in predicting aggregation propensity. MD simulations were also performed to explore structure-aggregation relationships for selected molecules, identifying chemical modifications that reversed the predicted behaviour of a given aggregator/non-aggregator compound. While lower throughput than rapid cheminformatics-based SCAM filters, MD-based prediction of aggregation has potential to be deployed on the scale of focused subsets of moderate size, and, depending on the target application, provide guidance on removing or optimizing a compound's aggregation propensity.

The European Lead Factory: Results from a decade of collaborative, public-private, drug discovery programs

The European Lead Factory (ELF) is a consortium of universities and small and medium-sized enterprises (SMEs) dedicated to drug discovery, and the pharmaceutical industry. This unprecedented consortium provides high-throughput screening, triage, and hit validation, including to non-consortium members. The ELF library was created through a novel compound-sharing model between nine pharmaceutical companies and expanded through library synthesis by chemistry-specialized SMEs. The library has been screened against ∼270 different targets and 15 phenotypic assays, and hits have been developed to form the basis of patents and spin-off companies. Here, we review the outcome of screening campaigns of the ELF, including the performance and physicochemical properties of the library, identification of possible frequent hitter compounds, and the effectiveness of the compound-sharing model.

Discovery of pyrimidoindol and benzylpyrrolyl inhibitors targeting SARS-CoV-2 main protease (Mpro) through pharmacophore modelling, covalent docking, and biological evaluation

The main protease (Mpro) enzyme has an imperative function in disease progression and the life cycle of the SARS-CoV-2 virus. Although the orally active drug nirmatrelvir (co-administered with ritonavir as paxlovid) has been approved for emergency use as the frontline antiviral agent, there are a number of limitations that necessitate the discovery of new drug scaffolds, such as poor pharmacokinetics and susceptibility to proteolytic degradation due to its peptidomimetic nature. This study utilized a novel virtual screening workflow that combines pharmacophore modelling, multiple-receptor covalent docking, and biological evaluation in order to find new Mpro inhibitors. After filtering and analysing ∼66,000 ligands from three different electrophilic libraries, 29 compounds were shortlisted for experimental testing, and two of them exhibited ≥20% inhibition at 100 μM. Our top candidate, GF04, is a benzylpyrrolyl compound that exhibited the highest inhibition activity of 38.3%, with a relatively small size (<350 Da) and leadlike character. Interestingly, our approach also identified another hit, DR07, a pyrimidoindol with a non-peptide character, and a molecular weight of 438.9 Da, reporting an inhibition of 26.3%. The established approach detailed in this study, in conjunction with the discovered inhibitors, has the capacity to yield novel perspectives for devising covalent inhibitors targeting the COVID-19 Mpro enzyme and other comparable targets.

Structure-Based Identification of Organoruthenium Compounds as Nanomolar Antagonists of Cannabinoid Receptors

Metal complexes exhibit a diverse range of coordination geometries, representing novel privileged scaffolds with convenient click types of preparation inaccessible for typical carbon-centered organic compounds. Herein, we explored the opportunity to identify biologically active organometallic complexes by reverse docking of a rigid, minimum-size octahedral organoruthenium scaffold against thousands of protein-binding pockets. Interestingly, cannabinoid receptor type 1 (CB1) was identified based on the docking scores and the degree of overlap between the docked organoruthenium scaffold and the hydrophobic scaffold of the cocrystallized ligand. Further structure-based optimization led to the discovery of organoruthenium complexes with nanomolar binding affinities and high selectivity toward CB2. Our work indicates that octahedral organoruthenium scaffolds may be advantageous for targeting the large and hydrophobic binding pockets and that the reverse docking approach may facilitate the discovery of novel privileged scaffolds, such as organometallic complexes, for exploring chemical space in lead discovery.

Generation of Molecular Counterfactuals for Explainable Machine Learning Based on Core-Substituent Recombination

The use of black box machine learning models whose decisions cannot be understood limits the acceptance of predictions in interdisciplinary research and camouflages artificial learning characteristics leading to predictions for other than anticipated reasons. Consequently, there is increasing interest in explainable artificial intelligence to rationalize predictions and uncover potential pitfalls. Among others, relevant approaches include feature attribution methods to identify molecular structures determining predictions and counterfactuals (CFs) or contrastive explanations. CFs are defined as variants of test instances with minimal modifications leading to opposing predictions. In medicinal chemistry, CFs have thus far only been little investigated although they are particularly intuitive from a chemical perspective. We introduce a new methodology for the systematic generation of CFs that is centered on well-defined structural analogues of test compounds. The approach is transparent, computationally straightforward, and shown to provide a wealth of CFs for test sets. The method is made freely available.

Identification of Triazolopyrimidinyl Scaffold SARS-CoV-2 Papain-Like Protease (PLpro) Inhibitor

The global impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its companion disease, COVID-19, has reminded us of the importance of basic coronaviral research. In this study, a comprehensive approach using molecular docking, in vitro assays, and molecular dynamics simulations was applied to identify potential inhibitors for SARS-CoV-2 papain-like protease (PLpro), a key and underexplored viral enzyme target. A focused protease inhibitor library was initially created and molecular docking was performed using CmDock software (v0.2.0), resulting in the selection of hit compounds for in vitro testing on the isolated enzyme. Among them, compound 372 exhibited promising inhibitory properties against PLpro, with an IC50 value of 82 ± 34 μM. The compound also displayed a new triazolopyrimidinyl scaffold not yet represented within protease inhibitors. Molecular dynamics simulations demonstrated the favorable binding properties of compound 372. Structural analysis highlighted its key interactions with PLpro, and we stress its potential for further optimization. Moreover, besides compound 372 as a candidate for PLpro inhibitor development, this study elaborates on the PLpro binding site dynamics and provides a valuable contribution for further efforts in pan-coronaviral PLpro inhibitor development.

Fragment-based drug nanoaggregation reveals drivers of self-assembly

Drug nanoaggregates are particles that can deleteriously cause false positive results during drug screening efforts, but alternatively, they may be used to improve pharmacokinetics when developed for drug delivery purposes. The structural features of molecules that drive nanoaggregate formation remain elusive, however, and the prediction of intracellular aggregation and rational design of nanoaggregate-based carriers are still challenging. We investigate nanoaggregate self-assembly mechanisms using small molecule fragments to identify the critical molecular forces that contribute to self-assembly. We find that aromatic groups and hydrogen bond acceptors/donors are essential for nanoaggregate formation, suggesting that both π-π stacking and hydrogen bonding are drivers of nanoaggregation. We apply structure-assembly-relationship analysis to the drug sorafenib and discover that nanoaggregate formation can be predicted entirely using drug fragment substructures. We also find that drug nanoaggregates are stabilized in an amorphous core-shell structure. These findings demonstrate that rational design can address intracellular aggregation and pharmacologic/delivery challenges in conventional and fragment-based drug development processes.

Anatomy of Potency Predictions Focusing on Structural Analogues with Increasing Potency Differences Including Activity Cliffs

Potency predictions are popular in compound design and optimization but are complicated by intrinsic limitations. Moreover, even for nonlinear methods, activity cliffs (ACs, formed by structural analogues with large potency differences) represent challenging test cases for compound potency predictions. We have devised a new test system for potency predictions, including AC compounds, that is based on partitioned matched molecular pairs (MMP) and makes it possible to monitor prediction accuracy at the level of analogue pairs with increasing potency differences. The results of systematic predictions using different machine learning and control methods on MMP-based data sets revealed increasing prediction errors when potency differences between corresponding training and test compounds increased, including large prediction errors for AC compounds. At the global level, these prediction errors were not apparent due to the statistical dominance of analogue pairs with small potency differences. Test compounds from such pairs were accurately predicted and determined the observed global prediction accuracy. Shapley value analysis, an explainable artificial intelligence approach, was applied to identify structural features determining potency predictions using different methods. The analysis revealed that numerical predictions of different regression models were determined by features that were shared by MMP partner compounds or absent in these compounds, with opposing effects. These findings provided another rationale for accurate predictions of similar potency values for structural analogues and failures in predicting the potency of AC compounds.

The Synthesis and Antibacterial Properties of Pillar[5]arene with Streptocide Fragments

The growing problem of bacterial resistance to antimicrobials actualizes the development of new approaches to solve this challenge. Supramolecular chemistry tools can overcome the limited bacterial resistance and side effects of classical sulfonamides that hinder their use in therapy. Here, we synthesized a number of pillar[5]arenes functionalized with different substituents, determined their ability to self-association using DLS, and characterized antimicrobial properties against S. typhimurium, K. pneumoniae, P. aeruginosa, S. epidermidis, S. aureus via a resazurin test. Biofilm prevention concentration was calculated for an agent with established antimicrobial activity by the crystal-violet staining method. We evaluated the mutagenicity of the macrocycle using the Ames test and its ability to affect the viability of A549 and LEK cells in the MTT-test. It was shown that macrocycle functionalized with sulfonamide residues exhibited antimicrobial activity an order higher than pure streptocide and also revealed the ability to prevent biofilm formation of S. aureus and P. aeruginosa. The compound did not show mutagenic activity and exhibited low toxicity to eukaryotic cells. The obtained results allow considering modification of the macrocyclic platforms with classic antimicrobials as an opportunity to give them a "second life" and return to practice with improved properties.

Colloidal aggregation confounds cell-based Covid-19 antiviral screens

Colloidal aggregation is one of the largest contributors to false-positives in early drug discovery and chemical biology. Much work has focused on its impact on pure-protein screens; here we consider aggregations role in cell-based infectivity assays in Covid-19 drug repurposing. We began by investigating the potential aggregation of 41 drug candidates reported as SARs-CoV-2 entry inhibitors. Of these, 17 formed colloidal-particles by dynamic light scattering and exhibited detergent-dependent enzyme inhibition. To evaluate antiviral efficacy of the drugs in cells we used spike pseudotyped lentivirus and pre-saturation of the colloids with BSA. The antiviral potency of the aggregators was diminished by at least 10-fold and often entirely eliminated in the presence of BSA, suggesting antiviral activity can be attributed to the non-specific nature of the colloids. In confocal microscopy, the aggregates induced fluorescent puncta of labeled spike protein, consistent with sequestration of the protein on the colloidal particles. Addition of either non-ionic detergent or of BSA disrupted these puncta. These observations suggest that colloidal aggregation is common among cell-based anti-viral drug repurposing, and perhaps cell-based assays more broadly, and offers rapid counter-screens to detect and eliminate these artifacts, allowing the community invest resources in compounds with true potential as a Covid-19 therapeutic.

Unraveling the unexpected aggregation behavior of Pyrazole-Based compounds Targeting Mycobacterium tuberculosis UDP-Galactopyranose mutase

A pyrazole-based compound, MS208, was previously identified as an inhibitor of UDP-Galactopyranose Mutase from Mycobacterium tuberculosis (MtUGM). Targeting this enzyme is a novel therapeutic strategy for the development of new antituberculosis agents because MtUGM is an essential enzyme for the bacterial cell wall synthesis and it is not present in human. It was proposed that MS208 targets an allosteric site in MtUGM as MS208 followed a mixed inhibition model. DA10, an MS208 analogue, showed competitive inhibition rather than mixed inhibition. In this paper, we have used an integrated biophysical approach, including thermal shift assays, dynamic light scattering and nuclear magnetic resonance experiments, to show that MS208 and many analogues displayed unexpected aggregation behavior against MtUGM.

Nonspecific membrane bilayer perturbations by ivermectin underlie SARS-CoV-2 in vitro activity

Since it was proposed as a potential host-directed antiviral agent for SARS-CoV-2, the antiparasitic drug ivermectin has been investigated thoroughly in clinical trials, which have provided insufficient support for its clinical efficacy. To examine the potential for ivermectin to be repurposed as an antiviral agent, we therefore undertook a series of preclinical studies. Consistent with early reports, ivermectin decreased SARS-CoV-2 viral burden in in vitro models at low micromolar concentrations, five- to ten-fold higher than the reported toxic clinical concentration. At similar concentrations, ivermectin also decreased cell viability and increased biomarkers of cytotoxicity and apoptosis. Further mechanistic and profiling studies revealed that ivermectin nonspecifically perturbs membrane bilayers at the same concentrations where it decreases the SARS-CoV-2 viral burden, resulting in nonspecific modulation of membrane-based targets such as G-protein coupled receptors and ion channels. These results suggest that a primary molecular mechanism for the in vitro antiviral activity of ivermectin may be nonspecific membrane perturbation, indicating that ivermectin is unlikely to be translatable into a safe and effective antiviral agent. These results and experimental workflow provide a useful paradigm for performing preclinical studies on (pandemic-related) drug repurposing candidates.

Rationalizing general limitations in assessing and comparing methods for compound potency prediction

Compound potency predictions play a major role in computational drug discovery. Predictive methods are typically evaluated and compared in benchmark calculations that are widely applied. Previous studies have revealed intrinsic limitations of potency prediction benchmarks including very similar performance of increasingly complex machine learning methods and simple controls and narrow error margins separating machine learning from randomized predictions. However, origins of these limitations are currently unknown. We have carried out an in-depth analysis of potential reasons leading to artificial outcomes of potency predictions using different methods. Potency predictions on activity classes typically used in benchmark settings were found to be determined by compounds with intermediate potency close to median values of the compound data sets. The potency of these compounds was consistently predicted with high accuracy, without the need for learning, which dominated the results of benchmark calculations, regardless of the activity classes used. Taken together, our findings provide a clear rationale for general limitations of compound potency benchmark predictions and a basis for the design of alternative test systems for methodological comparisons.

Discovering the Active Ingredients of Medicine and Food Homologous Substances for Inhibiting the Cyclooxygenase-2 Metabolic Pathway by Machine Learning Algorithms

Cyclooxygenase-2 (COX-2) and microsomal prostaglandin E2 synthase (mPGES-1) are two key targets in anti-inflammatory therapy. Medicine and food homology (MFH) substances have both edible and medicinal properties, providing a valuable resource for the development of novel, safe, and efficient COX-2 and mPGES-1 inhibitors. In this study, we collected active ingredients from 503 MFH substances and constructed the first comprehensive MFH database containing 27,319 molecules. Subsequently, we performed Murcko scaffold analysis and K-means clustering to deeply analyze the composition of the constructed database and evaluate its structural diversity. Furthermore, we employed four supervised machine learning algorithms, including support vector machine (SVM), random forest (RF), deep neural networks (DNNs), and eXtreme Gradient Boosting (XGBoost), as well as ensemble learning, to establish 640 classification models and 160 regression models for COX-2 and mPGES-1 inhibitors. Among them, ModelA_ensemble_RF_1 emerged as the optimal classification model for COX-2 inhibitors, achieving predicted Matthews correlation coefficient (MCC) values of 0.802 and 0.603 on the test set and external validation set, respectively. ModelC_RDKIT_SVM_2 was identified as the best regression model based on COX-2 inhibitors, with root mean squared error (RMSE) values of 0.419 and 0.513 on the test set and external validation set, respectively. ModelD_ECFP_SVM_4 stood out as the top classification model for mPGES-1 inhibitors, attaining MCC values of 0.832 and 0.584 on the test set and external validation set, respectively. The optimal regression model for mPGES-1 inhibitors, ModelF_3D_SVM_1, exhibited predictive RMSE values of 0.253 and 0.35 on the test set and external validation set, respectively. Finally, we proposed a ligand-based cascade virtual screening strategy, which integrated the well-performing supervised machine learning models with unsupervised learning: the self-organized map (SOM) and molecular scaffold analysis. Using this virtual screening workflow, we discovered 10 potential COX-2 inhibitors and 15 potential mPGES-1 inhibitors from the MFH database. We further verified candidates by molecular docking, investigated the interaction of the candidate molecules upon binding to COX-2 or mPGES-1. The constructed comprehensive MFH database has laid a solid foundation for the further research and utilization of the MFH substances. The series of well-performing machine learning models can be employed to predict the COX-2 and mPGES-1 inhibitory capabilities of unknown compounds, thereby aiding in the discovery of anti-inflammatory medications. The COX-2 and mPGES-1 potential inhibitor molecules identified through the cascade virtual screening approach provide insights and references for the design of highly effective and safe novel anti-inflammatory drugs.

Evaluating Known Zika Virus NS2B-NS3 Protease Inhibitor Scaffolds via In Silico Screening and Biochemical Assays

The NS2B-NS3 protease (NS2B-NS3pro) is regarded as an interesting molecular target for drug design, discovery, and development because of its essential role in the Zika virus (ZIKV) cycle. Although no NS2B-NS3pro inhibitors have reached clinical trials, the employment of drug-like scaffolds can facilitate the screening process for new compounds. In this study, we performed a combination of ligand-based and structure-based in silico methods targeting two known non-peptide small-molecule scaffolds with micromolar inhibitory activity against ZIKV NS2B-NS3pro by a virtual screening (VS) of promising compounds. Based on these two scaffolds, we selected 13 compounds from an initial library of 509 compounds from ZINC15's similarity search. These compounds exhibited structural modifications that are distinct from previously known compounds yet keep pertinent features for binding. Despite promising outcomes from molecular docking and initial enzymatic assays against NS2B-NS3pro, confirmatory assays with a counter-screening enzyme revealed an artifactual inhibition of the assessed compounds. However, we report two compounds, 9 and 11, that exhibited antiviral properties at a concentration of 50 μM in cellular-based assays. Overall, this study provides valuable insights into the ongoing research on anti-ZIKV compounds to facilitate and improve the development of new inhibitors.

New Scaffolds of Proteasome Inhibitors: Boosting Anticancer Potential by Exploiting the Synergy of In Silico and In Vitro Methodologies

Cancer is a complex multifactorial disease whose pathophysiology involves multiple metabolic pathways, including the ubiquitin-proteasome system, for which several proteasome inhibitors have already been approved for clinical use. However, the resistance to existing therapies and the occurrence of severe adverse effects is still a concern. The purpose of this study was the discovery of novel scaffolds of proteasome inhibitors with anticancer activity, aiming to overcome the limitations of the existing proteasome inhibitors. Thus, a structure-based virtual screening protocol was developed using the structure of the human 20S proteasome, and 246 compounds from virtual databases were selected for in vitro evaluation, namely proteasome inhibition assays and cell viability assays. Compound 4 (JHG58) was shortlisted as the best hit compound based on its potential in terms of proteasome inhibitory activity and its ability to induce cell death (both with IC50 values in the low micromolar range). Molecular docking studies revealed that compound 4 interacts with key residues, namely with the catalytic Thr1, Ala20, Thr21, Lys33, and Asp125 at the chymotrypsin-like catalytic active site. The hit compound is a good candidate for additional optimization through a hit-to-lead campaign.

Prediction of cancer nanomedicines self-assembled from meta-synergistic drug pairs

Combination therapy is widely used in cancer medicine due to the benefits of drug synergy and the reduction of acquired resistance. To minimize emergent toxicities, nanomedicines containing drug combinations are being developed, and they have shown encouraging results. However, developing multi-drug loaded nanoparticles is highly complex and lacks predictability. Previously, it was shown that single drugs can self-assemble with near-infrared dye, IR783, to form cancer-targeted nanoparticles. A structure-based predictive model showed that only 4% of the drug space self-assembles with IR783. Here, we mapped the self-assembly outcomes of 77 small molecule drugs and drug pairs with IR783. We found that the small molecule drug space can be divided into five types, and type-1 drugs self-assemble with three out of four possible drug types that do not form stable nanoparticles. To predict the self-assembly outcome of any drug pair, we developed a machine learning model based on decision trees, which was trained and tested with F1-scores of 89.3% and 87.2%, respectively. We used literature text mining to capture drug pairs with biological synergy together with synergistic chemical self-assembly and generated a database with 1985 drug pairs for 70 cancers. We developed an online search tool to identify cancer-specific, meta-synergistic drug pairs (both chemical and biological synergism) and validated three different pairs in vitro. Lastly, we discovered a novel meta-synergistic pair, bortezomib-cabozantinib, which formed stable nanoparticles with improved biodistribution, efficacy, and reduced toxicity, even over single drugs, in an in vivo model of head and neck cancer.

Fragment-based screening by protein-detected NMR spectroscopy

Fragment-based drug discovery (FBDD) identifies low molecular weight compounds that can be developed into ligands with high affinity and selectivity for therapeutic targets. Screening fragment libraries (<10,000 molecules) with biophysical techniques against macromolecules provides information about novel chemical spaces that bind the macromolecule and scaffolds that can be modified to increase potency. A fragment-screening pipeline requires a standardized protocol for target selection, library assembly and maintenance, library screening, and hit validation to ensure hit integrity. Herein, the fundamental aspects of a fragment screening pipeline-focusing on protein-detected NMR data collection and analysis-are discussed in detail for researchers to use as a resource in their FBDD projects. Selected screening targets must undergo rigorous stability and buffer testing by NMR spectroscopy to ensure the protein structure is stable for the entire screen. Biophysical instrumentation that rapidly measures protein thermostability is helpful in buffer screening. Molecules in fragment libraries are analyzed computationally and physically, stored at appropriate temperatures, and multiplexed in well plates for library conservation. The screening protocol is streamlined using liquid handling robotics for sample preparation and customized Python scripts for protein-detected NMR data analysis. Molecules identified from the screen are titrated to determine their binding site(s) and Kd values and confirmed with an orthogonal biophysical assay. This detailed FBDD screening pipeline developed by the Program in Chemical Biology at the Medical College of Wisconsin has successfully screened many unrelated target proteins to identified novel molecules that selectively bind to these target proteins.

Explaining Multiclass Compound Activity Predictions Using Counterfactuals and Shapley Values

Most machine learning (ML) models produce black box predictions that are difficult, if not impossible, to understand. In pharmaceutical research, black box predictions work against the acceptance of ML models for guiding experimental work. Hence, there is increasing interest in approaches for explainable ML, which is a part of explainable artificial intelligence (XAI), to better understand prediction outcomes. Herein, we have devised a test system for the rationalization of multiclass compound activity prediction models that combines two approaches from XAI for feature relevance or importance analysis, including counterfactuals (CFs) and Shapley additive explanations (SHAP). For compounds with different single- and dual-target activities, we identified small compound modifications that induce feature changes inverting class label predictions. In combination with feature mapping, CFs and SHAP value calculations provide chemically intuitive explanations for model decisions.

Computer-Aided Identification of Kinase-Targeted Small Molecules for Cancer: A Review on AKT Protein

AKT (also known as PKB) is a serine/threonine kinase that plays a pivotal regulatory role in the PI3K/AKT/mTOR signaling pathway. Dysregulation of AKT activity, especially its hyperactivation, is closely associated with the development of various human cancers and resistance to chemotherapy. Over the years, a wide array of AKT inhibitors has been discovered through experimental and computational approaches. In this regard, herein we present a comprehensive overview of AKT inhibitors identified using computer-assisted drug design methodologies (including docking-based and pharmacophore-based virtual screening, machine learning, and quantitative structure-activity relationships) and successfully validated small molecules endowed with anticancer activity. Thus, this review provides valuable insights to support scientists focused on AKT inhibition for cancer treatment and suggests untapped directions for future computer-aided drug discovery efforts.

Towards artificial intelligence-enabled extracellular vesicle precision drug delivery

Extracellular Vesicles (EVs), particularly exosomes, recently exploded into nanomedicine as an emerging drug delivery approach due to their superior biocompatibility, circulating stability, and bioavailability in vivo. However, EV heterogeneity makes molecular targeting precision a critical challenge. Deciphering key molecular drivers for controlling EV tissue targeting specificity is in great need. Artificial intelligence (AI) brings powerful prediction ability for guiding the rational design of engineered EVs in precision control for drug delivery. This review focuses on cutting-edge nano-delivery via integrating large-scale EV data with AI to develop AI-directed EV therapies and illuminate the clinical translation potential. We briefly review the current status of EVs in drug delivery, including the current frontier, limitations, and considerations to advance the field. Subsequently, we detail the future of AI in drug delivery and its impact on precision EV delivery. Our review discusses the current universal challenge of standardization and critical considerations when using AI combined with EVs for precision drug delivery. Finally, we will conclude this review with a perspective on future clinical translation led by a combined effort of AI and EV research.

Structure-Based Discovery of Inhibitors of the SARS-CoV-2 Nsp14 N7-Methyltransferase

An under-explored target for SARS-CoV-2 is the S-adenosyl methionine (SAM)-dependent methyltransferase Nsp14, which methylates the N7-guanosine of viral RNA at the 5'-end, allowing the virus to evade host immune response. We sought new Nsp14 inhibitors with three large library docking strategies. First, up to 1.1 billion lead-like molecules were docked against the enzyme's SAM site, leading to three inhibitors with IC50 values from 6 to 50 μM. Second, docking a library of 16 million fragments revealed 9 new inhibitors with IC50 values from 12 to 341 μM. Third, docking a library of 25 million electrophiles to covalently modify Cys387 revealed 7 inhibitors with IC50 values from 3.5 to 39 μM. Overall, 32 inhibitors encompassing 11 chemotypes had IC50 values < 50 μM and 5 inhibitors in 4 chemotypes had IC50 values < 10 μM. These molecules are among the first non-SAM-like inhibitors of Nsp14, providing starting points for future optimization.

Modeling the expansion of virtual screening libraries

Recently, 'tangible' virtual libraries have made billions of molecules readily available. Prioritizing these molecules for synthesis and testing demands computational approaches, such as docking. Their success may depend on library diversity, their similarity to bio-like molecules and how receptor fit and artifacts change with library size. We compared a library of 3 million 'in-stock' molecules with billion-plus tangible libraries. The bias toward bio-like molecules in the tangible library decreases 19,000-fold versus those 'in-stock'. Similarly, thousands of high-ranking molecules, including experimental actives, from five ultra-large-library docking campaigns are also dissimilar to bio-like molecules. Meanwhile, better-fitting molecules are found as the library grows, with the score improving log-linearly with library size. Finally, as library size increases, so too do rare molecules that rank artifactually well. Although the nature of these artifacts changes from target to target, the expectation of their occurrence does not, and simple strategies can minimize their impact.

Discovery of a novel dual functional phenylpyrazole-styryl hybrid that induces apoptotic and autophagic cell death in bladder cancer cells

Unpleasant side effects and resistance development remained the Achilles heel of chemotherapy. Since low tumor-selectivity and monotonous effect of chemotherapy are closely related to such bottleneck, targeting tumor-selective multi-functional anticancer agents may be an ideal strategy in the search of new safer drugs. Herein, we report the discovery of compound 21, a nitro-substituted 1,5-diphenyl-3-styryl-1H-pyrazole that possesses dual functional characteristics. The 2D- and 3D-culture-based studies revealed that 21 not only could induce ROS-independent apoptotic and EGFR/AKT/mTOR-mediated autophagic cell deaths in EJ28 cells simultaneously but also has the ability in inducing cell death at both proliferating and quiescent zones of EJ28 spheroids. The molecular modelling analysis showed that 21 possesses EGFR targeting capability as it forms stable interactions in the EGFR active site. Together with its good safety profile in the zebrafish-based model, the present study showed that 21 is promising and may lead to the discovery of tumor-selective multi-functional anti-cancer agents.

Differences in learning characteristics between support vector machine and random forest models for compound classification revealed by Shapley value analysis

The random forest (RF) and support vector machine (SVM) methods are mainstays in molecular machine learning (ML) and compound property prediction. We have explored in detail how binary classification models derived using these algorithms arrive at their predictions. To these ends, approaches from explainable artificial intelligence (XAI) are applicable such as the Shapley value concept originating from game theory that we adapted and further extended for our analysis. In large-scale activity-based compound classification using models derived from training sets of increasing size, RF and SVM with the Tanimoto kernel produced very similar predictions that could hardly be distinguished. However, Shapley value analysis revealed that their learning characteristics systematically differed and that chemically intuitive explanations of accurate RF and SVM predictions had different origins.

Large-Scale Predictions of Compound Potency with Original and Modified Activity Classes Reveal General Prediction Characteristics and Intrinsic Limitations of Conventional Benchmarking Calculations

Predicting compound potency is a major task in computational medicinal chemistry, for which machine learning is often applied. This study systematically predicted compound potency values for 367 target-based compound activity classes from medicinal chemistry using a preferred machine learning approach and simple control methods. The predictions produced unexpectedly similar results for different classes and comparably high accuracy for machine learning and simple control models. Based on these findings, the influence of different data set modifications on relative prediction accuracies was explored, including potency range balancing, removal of nearest neighbors, and analog series-based compound partitioning. The predictions were surprisingly resistant to these modifications, leading to only small error margin increases. These findings also show that conventional benchmark settings are unsuitable for directly comparing potency prediction methods.

The evolving role of investigative toxicology in the pharmaceutical industry

For decades, preclinical toxicology was essentially a descriptive discipline in which treatment-related effects were carefully reported and used as a basis to calculate safety margins for drug candidates. In recent years, however, technological advances have increasingly enabled researchers to gain insights into toxicity mechanisms, supporting greater understanding of species relevance and translatability to humans, prediction of safety events, mitigation of side effects and development of safety biomarkers. Consequently, investigative (or mechanistic) toxicology has been gaining momentum and is now a key capability in the pharmaceutical industry. Here, we provide an overview of the current status of the field using case studies and discuss the potential impact of ongoing technological developments, based on a survey of investigative toxicologists from 14 European-based medium-sized to large pharmaceutical companies.

Mutual Cooperativity of Three Allosteric Sites on the Dopamine D1 Receptor

An amine-containing molecule called Compound A has been reported by a group from Bristol-Myers Squibb to act as a positive allosteric modulator (PAM) at the dopamine D1 receptor. We synthesized the more active enantiomer of Compound A (BMS-A1) and compared it with the D1 PAMs DETQ and MLS6585, which are known to bind to intracellular loop 2 and the extracellular portion of transmembrane helix 7, respectively. Results from D1/D5 chimeras indicated that PAM activity of BMS-A1 tracked with the presence of D1 sequence in the N-terminal/extracellular region of the D1 receptor, a unique location compared with either of the other PAMs. In pairwise combinations, BMS-A1 potentiated the small allo-agonist activity of each of the other PAMs, while the triple PAM combination (in the absence of dopamine) produced a cAMP response about 64% of the maximum produced by dopamine. Each of the pairwise PAM combinations produced a much larger leftward shift of the dopamine EC₅₀ than either single PAM alone. All three PAMs in combination produced a 1000-fold leftward shift of the dopamine curve. These results demonstrate the presence of three non-overlapping allosteric sites that cooperatively stabilize the same activated state of the human D1 receptor. SIGNIFICANCE STATEMENT: Deficiencies in dopamine D1 receptor activation are seen in Parkinson disease and other neuropsychiatric disorders. In this study, three positive allosteric modulators of the dopamine D1 receptor were found to bind to distinct and separate sites, interacting synergistically with each other and dopamine, with the triple combination causing a 1000-fold leftward shift of the response to dopamine. These results showcase multiple opportunities to modulate D1 tone and highlight new pharmacological approaches for allosteric modulation of G-protein-coupled receptors.

A high-throughput screening campaign against PFKFB3 identified potential inhibitors with novel scaffolds

The growth of solid tumors depends on tumor vascularization and the endothelial cells (ECs) that line the lumen of blood vessels. ECs generate a large fraction of ATP through glycolysis, and elevation of their glycolytic activity is associated with angiogenic behavior in solid tumors. 6-Phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3) positively regulates glycolysis via fructose-2/6-bisphosphate, the product of its kinase activity. Partial inhibition of glycolysis in tumor ECs by targeting PFKFB3 normalizes the otherwise abnormal tumor vessels, thereby reducing metastasis and improving the outcome of chemotherapy. Although a limited number of tool compounds exist, orally available PFKFB3 inhibitors are unavailable. In this study we conducted a high-throughput screening campaign against the kinase activity of PFKFB3, involving 250,240 chemical compounds. A total of 507 initial hits showing >50% inhibition at 20 µM were identified, 66 of them plus 1 analog from a similarity search consistently displayed low IC50 values (<10 µM). In vitro experiments yielded 22 nontoxic hits that suppressed the tube formation of primary human umbilical vein ECs at 10 µM. Of them, 15 exhibited binding affinity to PFKFB3 in surface plasmon resonance assays, including 3 (WNN0403-E003, WNN1352-H007 and WNN1542-F004) that passed the pan-assay interference compounds screening without warning flags. This study provides potential leads to the development of new PFKFB3 inhibitors.

Early-phase drug discovery of β-III-spectrin actin-binding modulators for treatment of spinocerebellar ataxia type 5

β-III-Spectrin is a key cytoskeletal protein that localizes to the soma and dendrites of cerebellar Purkinje cells and is required for dendritic arborization and signaling. A spinocerebellar ataxia type 5 L253P mutation in the cytoskeletal protein β-III-spectrin causes high-affinity actin binding. Previously we reported a cell-based fluorescence assay for identification of small-molecule actin-binding modulators of the L253P mutant β-III-spectrin. Here we describe a complementary, in vitro, fluorescence resonance energy transfer (FRET) assay that uses purified L253P β-III-spectrin actin-binding domain (ABD) and F-actin. To validate the assay for high-throughput compatibility, we first confirmed that our 50% FRET signal was responsive to swinholide A, an actin-severing compound, and that this yielded excellent assay quality with a Z' value > 0.77. Second, we screened a 2684-compound library of US Food and Drug Administration-approved drugs. Importantly, the screening identified numerous compounds that decreased FRET between fluorescently labeled L253P ABD and F-actin. The activity and target of multiple Hit compounds were confirmed in orthologous cosedimentation actin-binding assays. Through future medicinal chemistry, the Hit compounds can potentially be developed into a spinocerebellar ataxia type 5-specific therapeutic. Furthermore, our validated FRET-based in vitro high-throughput screening platform is poised for screening large compound libraries for β-III-spectrin ABD modulators.

A mechanism of salt bridge-mediated resistance to FtsZ inhibitor PC190723 revealed by a cell-based screen

Bacterial cell division proteins, especially the tubulin homologue FtsZ, have emerged as strong targets for developing new antibiotics. Here, we have utilized the fission yeast heterologous expression system to develop a cell-based assay to screen for small molecules that directly and specifically target the bacterial cell division protein FtsZ. The strategy also allows for simultaneous assessment of the toxicity of the drugs to eukaryotic yeast cells. As a proof-of-concept of the utility of this assay, we demonstrate the effect of the inhibitors sanguinarine, berberine, and PC190723 on FtsZ. Though sanguinarine and berberine affect FtsZ polymerization, they exert a toxic effect on the cells. Further, using this assay system, we show that PC190723 affects Helicobacter pylori FtsZ function and gain new insights into the molecular determinants of resistance to PC190723. On the basis of sequence and structural analysis and site-specific mutations, we demonstrate that the presence of salt bridge interactions between the central H7 helix and β-strands S9 and S10 mediates resistance to PC190723 in FtsZ. The single-step in vivo cell-based assay using fission yeast enabled us to dissect the contribution of sequence-specific features of FtsZ and cell permeability effects associated with bacterial cell envelopes. Thus, our assay serves as a potent tool to rapidly identify novel compounds targeting polymeric bacterial cytoskeletal proteins like FtsZ to understand how they alter polymerization dynamics and address resistance determinants in targets.

In Silico Discovery of Small Molecule Modulators Targeting the Achilles' Heel of SARS-CoV-2 Spike Protein

The spike protein of SARS-CoV-2 has been a promising target for developing vaccines and therapeutics due to its crucial role in the viral entry process. Previously reported cryogenic electron microscopy (cryo-EM) structures have revealed that free fatty acids (FFA) bind with SARS-CoV-2 spike protein, stabilizing its closed conformation and reducing its interaction with the host cell target in vitro. Inspired by these, we utilized a structure-based virtual screening approach against the conserved FFA-binding pocket to identify small molecule modulators of SARS-CoV-2 spike protein, which helped us identify six hits with micromolar binding affinities. Further evaluation of their commercially available and synthesized analogs enabled us to discover a series of compounds with better binding affinities and solubilities. Notably, our identified compounds exhibited similar binding affinities against the spike proteins of the prototypic SARS-CoV-2 and a currently circulating Omicron BA.4 variant. Furthermore, the cryo-EM structure of the compound SPC-14 bound spike revealed that SPC-14 could shift the conformational equilibrium of the spike protein toward the closed conformation, which is human ACE2 (hACE2) inaccessible. Our identified small molecule modulators targeting the conserved FFA-binding pocket could serve as the starting point for the future development of broad-spectrum COVID-19 intervention treatments.

Transformation of a Metal Chelate into a "Catch and Anchor" Inhibitor of Botulinum A Protease

Targeting the botulinum neurotoxin light chain (LC) metalloprotease using small-molecule metal chelate inhibitors is a promising approach to counter the effects of the lethal toxin. However, to overcome the pitfalls associated with simple reversible metal chelate inhibitors, it is crucial to investigate alternative scaffolds/strategies. In conjunction with Atomwise Inc., in silico and in vitro screenings were conducted, yielding a number of leads, including a novel 9-hydroxy-4H-pyrido [1,2-a]pyrimidin-4-one (PPO) scaffold. From this structure, an additional series of 43 derivatives were synthesized and tested, resulting in a lead candidate with a K_i of 150 nM in a BoNT/A LC enzyme assay and 17 µM in a motor neuron cell-based assay. These data combined with structure-activity relationship (SAR) analysis and docking led to a bifunctional design strategy, which we termed "catch and anchor" for the covalent inhibition of BoNT/A LC. Kinetic evaluation was conducted on structures prepared from this catch and anchor campaign, providing k_inact/K_i values, and rationale for inhibition seen. Covalent modification was validated through additional assays, including an FRET endpoint assay, mass spectrometry, and exhaustive enzyme dialysis. The data presented support the PPO scaffold as a novel candidate for targeted covalent inhibition of BoNT/A LC.

Predicting Potent Compounds Using a Conditional Variational Autoencoder Based upon a New Structure-Potency Fingerprint

Prediction of the potency of bioactive compounds generally relies on linear or nonlinear quantitative structure-activity relationship (QSAR) models. Nonlinear models are generated using machine learning methods. We introduce a novel approach for potency prediction that depends on a newly designed molecular fingerprint (FP) representation. This structure-potency fingerprint (SPFP) combines different modules accounting for the structural features of active compounds and their potency values in a single bit string, hence unifying structure and potency representation. This encoding enables the derivation of a conditional variational autoencoder (CVAE) using SPFPs of training compounds and apply the model to predict the SPFP potency module of test compounds using only their structure module as input. The SPFP-CVAE approach correctly predicts the potency values of compounds belonging to different activity classes with an accuracy comparable to support vector regression (SVR), representing the state-of-the-art in the field. In addition, highly potent compounds are predicted with very similar accuracy as SVR and deep neural networks.

Targeting ecto-5'-nucleotidase: A comprehensive review into small molecule inhibitors and expression modulators

The purinergic signaling has drawn attention from academia and more recently from pharmaceutical industries as a potential therapeutic route for cancer treatment, since ATP may act as chemotactic agent and possess in vitro antineoplastic activity. On the other way, adenosine, produced in extracellular medium by ecto-5'-NT, acts as immunosuppressor and is related to neoangiogenesis, vasculogenesis and evasion to the immune system. Consequently, inhibitors of ecto-5'-NT may prevent tumor progression, reducing adenosine concentrations, preventing escape from the host's immune system and slowing cancer's growth. This review aims to highlight important biochemical and structural features of ecto-5'NT, highlight its expression profile in normal and cancer cell lines detailing compounds which may act as expression regulators and to review the several classes of ecto-5'NT inhibitors developed in the past 12 years, in order to build a general structure-activity relationship model to guide further compound design.

Novel inhibitors of breast cancer resistance protein (BCRP, ABCG2) among marketed drugs

Drug-drug interactions (DDIs) are a major concern for the safe use of medications. Breast cancer resistance protein (BCRP) is a clinically relevant ATP-binding cassette (ABC) transporter for drug disposition. Inhibition of BCRP increases the plasma concentrations of BCRP substrate drugs, which potentially could lead to adverse drug reactions. The aim of the present study was to identify BCRP inhibitors amongst a library of 232 commonly used drugs and anticancer drugs approved by the United States Food and Drug Administration (FDA). BCRP inhibition studies were carried out using the vesicular transport assay. We found 75 drugs that reduced the relative transport activity of BCRP to less than 25% of the vehicle control and were categorized as strong inhibitors. The concentration required for 50% inhibition (IC₅₀) was determined for 13 strong inhibitors that were previously poorly characterized for BCRP inhibition. The IC₅₀ ranged from 1.1 to 11 µM, with vemurafenib, dabigatran etexilate and everolimus being the strongest inhibitors. According to the drug interaction guidance documents from the FDA and the European Medicines Agency (EMA), in vivo DDI studies are warranted if the theoretical intestinal luminal concentration of a drug exceeds its IC₅₀ by tenfold. Here, the IC₅₀ values for eight of the drugs were 100-fold lower than their theoretical intestinal luminal concentration. Moreover, a mechanistic static model suggested that vemurafenib, bexarotene, dabigatran etexilate, rifapentine, aprepitant, and ivacaftor could almost fully inhibit intestinal BCRP, increasing the exposure of concomitantly administered rosuvastatin over 90%. Therefore, clinical studies are warranted to investigate whether these drugs cause BCRP-mediated DDIs in humans.

In silico studies, X-ray diffraction analysis and biological investigation of fluorinated pyrrolylated-chalcones in zebrafish epilepsy models

Epilepsy is the third most common known brain disease worldwide. Several antiepileptic drugs (AEDs) are available to improve seizure control. However, the associated side effects limit their practical use and highlight the ongoing search for safer and effective AEDs. Eighteen newly designed fluorine-containing pyrrolylated chalcones were extensively studied in silico, synthesized, structurally analyzed by X-ray diffraction (XRD), and biologically and toxicologically tested as potential new AEDs in zebrafish epilepsy in vivo models. The results predicted that 3-(3,5-difluorophenyl)-1-(1H-pyrrol-2-yl)prop-2-en-1-one (compound 8) had a good drug-like profile with binding affinity to γ-aminobutyric acid receptor type-A (GABA_A, -8.0 kcal/mol). This predicted active compound 8 was effective in reducing convulsive behaviour in pentylenetetrazol (PTZ)-induced larvae and hyperactive movements in zc4h2 knockout (KO) zebrafish, experimentally. Moreover, no cardiotoxic effect of compound 8 was observed in zebrafish. Overall, pyrrolylated chalcones could serve as alternative AEDs and warrant further in-depth pharmacological studies to uncover their mechanism of action.

Discovery of Natural Bisbenzylisoquinoline Analogs from the Library of Thai Traditional Plants as SARS-CoV-2 3CLPro Inhibitors: In Silico Molecular Docking, Molecular Dynamics, and In Vitro Enzymatic Activity

The emergence of SARS-CoV-2 in December 2019 has become a global issue due to the continuous upsurge in patients and the lack of drug efficacy for treatment. SARS-CoV-2 3CL^Pro is one of the most intriguing biomolecular targets among scientists worldwide for developing antiviral drugs due to its relevance in viral replication and transcription. Herein, we utilized computer-assisted drug screening to investigate 326 natural products from Thai traditional plants using structure-based virtual screening against SARS-CoV-2 3CL^Pro. Following the virtual screening, the top 15 compounds based on binding energy and their interactions with key amino acid Cys145 were obtained. Subsequently, they were further evaluated for protein-ligand complex stability via molecular dynamics simulation and binding free energy calculation using molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) approaches. Following drug-likeness and ADME/Tox assessments, seven bisbenzylisoquinolines were obtained, including neferine (3), liensinine (4), isoliensinine (5), dinklacorine (8), tiliacorinine (13), 2'-nortiliacorinine (14), and yanangcorinine (15). These compounds computationally showed a higher binding affinity than native N3 and GC-373 inhibitors and attained stable interactions on the active site of 3CL^pro during 100 ns in molecular dynamics (MD) simulation. Moreover, the in vitro enzymatic assay showed that most bisbenzylisoquinolines could experimentally inhibit SARS-CoV-2 3CL^Pro. To our delight, isoliensinine (5) isolated from Nelumbo nucifera demonstrated the highest inhibition of protease activity with the IC₅₀ value of 29.93 μM with low toxicity on Vero cells. Our findings suggested that bisbenzylisoquinoline scaffolds could be potentially used as an in vivo model for the development of effective anti-SARS-CoV-2 drugs.

Explaining Accurate Predictions of Multitarget Compounds with Machine Learning Models Derived for Individual Targets

In drug discovery, compounds with well-defined activity against multiple targets (multitarget compounds, MT-CPDs) provide the basis for polypharmacology and are thus of high interest. Typically, MT-CPDs for polypharmacology have been discovered serendipitously. Therefore, over the past decade, computational approaches have also been adapted for the design of MT-CPDs or their identification via computational screening. Such approaches continue to be under development and are far from being routine. Recently, different machine learning (ML) models have been derived to distinguish between MT-CPDs and corresponding compounds with activity against the individual targets (single-target compounds, ST-CPDs). When evaluating alternative models for predicting MT-CPDs, we discovered that MT-CPDs could also be accurately predicted with models derived for corresponding ST-CPDs; this was an unexpected finding that we further investigated using explainable ML. The analysis revealed that accurate predictions of ST-CPDs were determined by subsets of structural features of MT-CPDs required for their prediction. These findings provided a chemically intuitive rationale for the successful prediction of MT-CPDs using different ML models and uncovered general-feature subset relationships between MT- and ST-CPDs with activities against different targets.

Mechanistic Insight of Synthesized 1,4-Dihydropyridines as an Antidiabetic Sword against Reactive Oxygen Species

The pharmacologically privileged DHP derivatives were synthesized using the pragmatic multicomponent Hantzsch synthesis to screen the antidiabetic activity. Initially, the candidates were screened using an in vivo blood glucose test, where compound 8b showed the most prominent antidiabetic effect (% potency = 218%) compared to glimepiride. Then, a propositioned structure-activity relationship study was executed to reveal that longer side chains decreased the DHP's antidiabetic action. Mechanistically, compound 8b diminished ROS in β-cells and muscle cells simultaneously, which was proved by enhanced serum biochemical markers. Also, compound 8b decreased blood glucose by α-glucosidase inhibition (IC₅₀ = 4.48 ± 0.32 μM), compared to acarbose (7.40 ± 0.41 μM), based selectively on the plasma window of 8b. Acarbose demonstrated auspicious inhibitor activity according to the binding affinity (ΔG_binding), which was slightly lower than that of compound 8b (-54.7 and -46.8 kcal/mol, respectively). During the 100 ns molecular dynamics simulations, the structural and energetic assessments exposed the high consistency of compound 8b to bind to the α-glucosidase.

Inhibition of mutationally activated HER2

Human epidermal growth factor receptor 2 (HER2) is an oncogenic driver and key therapeutic target for human cancers. Current therapies targeting HER2 are primarily based on overexpression of the wild-type form of HER2. However, kinase domain mutations have been identified that can increase the activity of HER2 even when expressed at basal levels. Using purified enzymes, we confirmed the hyperactivity of two HER2 mutants (D769Y and P780insGSP). To identify small molecule inhibitors against these cancer-associated variants, we used a combined approach consisting of biochemical testing, similarity-based searching, and in silico modeling. These approaches resulted in the identification of a candidate molecule that inhibits mutant forms of HER2 in vitro and in cell-based assays. Our structural model predicts that the compound takes advantage of water-mediated interactions in the HER2 kinase binding pocket.

Exploring potential inhibitors against Kyasanur forest disease by utilizing molecular dynamics simulations and ensemble docking

Kyasanur forest disease (KFD) is a tick-borne, neglected tropical disease, caused by KFD virus (KFDV) which belongs to Flavivirus (Flaviviridae family). This emerging viral disease is a major threat to humans. Currently, vaccination is the only controlling method against the KFDV, and its effectiveness is very low. An effective control strategy is required to combat this emerging tropical disease using the existing resources. In this regard, in silico drug repurposing method offers an effective strategy to find suitable antiviral drugs against KFDV proteins. Drug repurposing is an effective strategy to identify new use for approved or investigational drugs that are outside the scope of their initial usage and the repurposed drugs have lower risk and higher safety compared to de novo developed drugs, because their toxicity and safety issues are profoundly investigated during the preclinical trials in human/other models. In the present work, we evaluated the effectiveness of the FDA approved and natural compounds against KFDV proteins using in silico molecular docking and molecular simulations. At present, no experimentally solved 3D structures for the KFD viral proteins are available in Protein Data Bank and hence their homology model was developed and used for the analysis. The present analysis successfully developed the reliable homology model of NS3 of KFDV, in terms of geometry and energy contour. Further, in silico molecular docking and molecular dynamics simulations successfully presented four FDA approved drugs and one natural compound against the NS3 homology model of KFDV. Communicated by Ramaswamy H. Sarma.