Novel paradigms for drug discovery: computational multitarget screening

GENIX enables comparative network analysis of single-cell RNA sequencing to reveal signatures of therapeutic interventions

Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cellular responses to perturbations such as therapeutic interventions and vaccines. Gene relevance to such perturbations is often assessed through differential expression analysis (DEA), which offers a one-dimensional view of the transcriptomic landscape. This method potentially overlooks genes with modest expression changes but profound downstream effects and is susceptible to false positives. We present GENIX (gene expression network importance examination), a computational framework that transcends DEA by constructing gene association networks and employing a network-based comparative model to identify topological signature genes. We benchmark GENIX using both synthetic and experimental datasets, including analysis of influenza vaccine-induced immune responses in peripheral blood mononuclear cells (PBMCs) from recovered COVID-19 patients. GENIX successfully emulates key characteristics of biological networks and reveals signature genes that are missed by classical DEA, thereby broadening the scope of target gene discovery in precision medicine.

Novel Therapies in APOL1-Mediated Kidney Disease: From Molecular Pathways to Therapeutic Options

Apolipoprotein L1 (APOL1) high-risk variants confer an increased risk for the development and progression of kidney disease among individuals of recent African ancestry. Over the past several years, significant progress has been made in understanding the pathogenesis of APOL1-mediated kidney diseases (AMKD), including genetic regulation, environmental interactions, immunomodulatory, proinflammatory and apoptotic signaling processes, as well as the complex role of APOL1 as an ion channel. Collectively, these findings have paved the way for novel therapeutic strategies to mitigate APOL1-mediated kidney injury. Precision medicine approaches are being developed to identify subgroups of AMKD patients who may benefit from these targeted interventions, fueling hope for improved clinical outcomes. This review summarizes key mechanistic insights in the pathogenesis of AMKD, emergent therapies, and discusses future challenges.

Imidazoles as Serotonin Receptor Modulators for Treatment of Depression: Structural Insights and Structure-Activity Relationship Studies

Serotoninergic signaling is identified as a crucial player in psychiatric disorders (notably depression), presenting it as a significant therapeutic target for treating such conditions. Inhibitors of serotoninergic signaling (especially selective serotonin reuptake inhibitors (SSRI) or serotonin and norepinephrine reuptake inhibitors (SNRI)) are prominently selected as first-line therapy for the treatment of depression, which benefits via increasing low serotonin levels and norepinephrine by blocking serotonin/norepinephrine reuptake and thereby increasing activity. While developing newer heterocyclic scaffolds to target/modulate the serotonergic systems, imidazole-bearing pharmacophores have emerged. The imidazole-derived pharmacophore already demonstrated unique structural characteristics and an electron-rich environment, ultimately resulting in a diverse range of bioactivities. Therefore, the current manuscript discloses such a specific modification and structural activity relationship (SAR) of attempted derivatization in terms of the serotonergic efficacy of the resultant inhibitor. We also featured a landscape of imidazole-based development, focusing on SAR studies against the serotoninergic system to target depression. This study covers the recent advancements in synthetic methodologies for imidazole derivatives and the development of new molecules having antidepressant activity via modulating serotonergic systems, along with their SAR studies. The focus of the study is to provide structural insights into imidazole-based derivatives as serotonergic system modulators for the treatment of depression.

Multiscale Analysis and Validation of Effective Drug Combinations Targeting Driver KRAS Mutations in Non-Small Cell Lung Cancer

Pharmacogenomics is a rapidly growing field with the goal of providing personalized care to every patient. Previously, we developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform for multiscale therapeutic discovery to screen optimal compounds for any indication/disease by performing analytics on their interactions using large protein libraries. We implemented a comprehensive precision medicine drug discovery pipeline within the CANDO platform to determine which drugs are most likely to be effective against mutant phenotypes of non-small cell lung cancer (NSCLC) based on the supposition that drugs with similar interaction profiles (or signatures) will have similar behavior and therefore show synergistic effects. CANDO predicted that osimertinib, an EGFR inhibitor, is most likely to synergize with four KRAS inhibitors.Validation studies with cellular toxicity assays confirmed that osimertinib in combination with ARS-1620, a KRAS G12C inhibitor, and BAY-293, a pan-KRAS inhibitor, showed a synergistic effect on decreasing cellular proliferation by acting on mutant KRAS. Gene expression studies revealed that MAPK expression is strongly correlated with decreased cellular proliferation following treatment with KRAS inhibitor BAY-293, but not treatment with ARS-1620 or osimertinib. These results indicate that our precision medicine pipeline may be used to identify compounds capable of synergizing with inhibitors of KRAS G12C, and to assess their likelihood of becoming drugs by understanding their behavior at the proteomic/interactomic scales.

Design, Synthesis, and Pharmacological Evaluation of N-Propargylated Diphenylpyrimidines as Multitarget Directed Ligands for the Treatment of Alzheimer's Disease

Alzheimer's disease (AD), a multifactorial complex neural disorder, is categorized with progressive memory loss and cognitive impairment as main clinical features. The multitarget directed ligand (MTDL) strategy is explored for the treatment of multifactorial diseases such as cancer and AD. Herein, we report the synthesis and screening of 24 N-propargyl-substituted diphenylpyrimidine derivatives as MTDLs against acetylcholine/butyrylcholine esterases and monoamine oxidase enzymes. In this series, VP1 showed the most potent MAO-B inhibitory activity with an IC₅₀ value of 0.04 ± 0.002 μM. VP15 with an IC₅₀ value of 0.04 ± 0.003 μM and a selectivity index of 626 (over BuChE) displayed the most potent AChE inhibitory activity in this series. In the reactive oxygen species (ROS) inhibition studies, VP1 reduced intercellular ROS levels in SH-SY5Y cells by 36%. This series of compounds also exhibited potent neuroprotective potential against 6-hydroxydopamine-induced neuronal damage in SH-SY5Y cells with up to 90% recovery. In the in vivo studies in the rats, the hydrochloride salt of VP15 was orally administered and found to cross the blood-brain barrier and reach the target site. VP15·HCl significantly attenuated the spatial memory impairment and improved the cognitive deficits in the mice. This series of compounds were found to be irreversible inhibitors and showed no cytotoxicity against neuronal cells. In in silico studies, the compounds attained thermodynamically stable orientation with complete occupancy at the active site of the receptors. Thus, N-propargyl-substituted diphenylpyrimidines displayed drug-like characteristics and have the potential to be developed as MTDLs for the effective treatment of AD.

A dual-target herbicidal inhibitor of lysine biosynthesis

Herbicides with novel modes of action are urgently needed to safeguard global agricultural industries against the damaging effects of herbicide-resistant weeds. We recently developed the first herbicidal inhibitors of lysine biosynthesis, which provided proof-of-concept for a promising novel herbicide target. In this study, we expanded upon our understanding of the mode of action of herbicidal lysine biosynthesis inhibitors. We previously postulated that these inhibitors may act as proherbicides. Here, we show this is not the case. We report an additional mode of action of these inhibitors, through their inhibition of a second lysine biosynthesis enzyme, and investigate the molecular determinants of inhibition. Furthermore, we extend our herbicidal activity analyses to include a weed species of global significance.

Identifying Protein Features and Pathways Responsible for Toxicity Using Machine Learning and Tox21: Implications for Predictive Toxicology

Humans are exposed to numerous compounds daily, some of which have adverse effects on health. Computational approaches for modeling toxicological data in conjunction with machine learning algorithms have gained popularity over the last few years. Machine learning approaches have been used to predict toxicity-related biological activities using chemical structure descriptors. However, toxicity-related proteomic features have not been fully investigated. In this study, we construct a computational pipeline using machine learning models for predicting the most important protein features responsible for the toxicity of compounds taken from the Tox21 dataset that is implemented within the multiscale Computational Analysis of Novel Drug Opportunities (CANDO) therapeutic discovery platform. Tox21 is a highly imbalanced dataset consisting of twelve in vitro assays, seven from the nuclear receptor (NR) signaling pathway and five from the stress response (SR) pathway, for more than 10,000 compounds. For the machine learning model, we employed a random forest with the combination of Synthetic Minority Oversampling Technique (SMOTE) and the Edited Nearest Neighbor (ENN) method (SMOTE+ENN), which is a resampling method to balance the activity class distribution. Within the NR and SR pathways, the activity of the aryl hydrocarbon receptor (NR-AhR) and the mitochondrial membrane potential (SR-MMP) were two of the top-performing twelve toxicity endpoints with AUCROCs of 0.90 and 0.92, respectively. The top extracted features for evaluating compound toxicity were analyzed for enrichment to highlight the implicated biological pathways and proteins. We validated our enrichment results for the activity of the AhR using a thorough literature search. Our case study showed that the selected enriched pathways and proteins from our computational pipeline are not only correlated with AhR toxicity but also form a cascading upstream/downstream arrangement. Our work elucidates significant relationships between protein and compound interactions computed using CANDO and the associated biological pathways to which the proteins belong for twelve toxicity endpoints. This novel study uses machine learning not only to predict and understand toxicity but also elucidates therapeutic mechanisms at a proteomic level for a variety of toxicity endpoints.

Virtual Screening of Drug-Like Compounds as Potential Inhibitors of the Dengue Virus NS5 Protein

Dengue virus (DENV) is the causative agent of dengue fever. Annually, there are about 400 million new cases of dengue worldwide, and so far there is no specific treatment against this disease. The NS5 protein is the largest and most conserved viral protein among flaviviruses and is considered a therapeutic target of great interest. This study aims to search drug-like compounds for possible inhibitors of the NS5 protein in the four serotypes of DENV. Using a virtual screening from a ∼642,759-compound database, we suggest 18 compounds with NS5 binding and highlight the best compound per region, in the methyltransferase and RNA-dependent RNA polymerase domains. These compounds interact mainly with the amino acids of the catalytic sites and/or are involved in processes of protein activity. The identified compounds presented physicochemical and pharmacological properties of interest for their use as possible drugs; furthermore, we found that some of these compounds do not affect cell viability in Huh-7; therefore, we suggest evaluating these compounds in vitro as candidates in future research.

Optimal COVID-19 therapeutic candidate discovery using the CANDO platform

The worldwide outbreak of SARS-CoV-2 in early 2020 caused numerous deaths and unprecedented measures to control its spread. We employed our Computational Analysis of Novel Drug Opportunities (CANDO) multiscale therapeutic discovery, repurposing, and design platform to identify small molecule inhibitors of the virus to treat its resulting indication, COVID-19. Initially, few experimental studies existed on SARS-CoV-2, so we optimized our drug candidate prediction pipelines using results from two independent high-throughput screens against prevalent human coronaviruses. Ranked lists of candidate drugs were generated using our open source cando.py software based on viral protein inhibition and proteomic interaction similarity. For the former viral protein inhibition pipeline, we computed interaction scores between all compounds in the corresponding candidate library and eighteen SARS-CoV proteins using an interaction scoring protocol with extensive parameter optimization which was then applied to the SARS-CoV-2 proteome for prediction. For the latter similarity based pipeline, we computed interaction scores between all compounds and human protein structures in our libraries then used a consensus scoring approach to identify candidates with highly similar proteomic interaction signatures to multiple known anti-coronavirus actives. We published our ranked candidate lists at the very beginning of the COVID-19 pandemic. Since then, 51 of our 276 predictions have demonstrated anti-SARS-CoV-2 activity in published clinical and experimental studies. These results illustrate the ability of our platform to rapidly respond to emergent pathogens and provide greater evidence that treating compounds in a multitarget context more accurately describes their behavior in biological systems.

Evaluating the performance of drug-repurposing technologies

Drug-repurposing technologies are growing in number and maturing. However, comparisons to each other and to reality are hindered because of a lack of consensus with respect to performance evaluation. Such comparability is necessary to determine scientific merit and to ensure that only meaningful predictions from repurposing technologies carry through to further validation and eventual patient use. Here, we review and compare performance evaluation measures for these technologies using version 2 of our shotgun repurposing Computational Analysis of Novel Drug Opportunities (CANDO) platform to illustrate their benefits, drawbacks, and limitations. Understanding and using different performance evaluation metrics ensures robust cross-platform comparability, enabling us to continue to strive toward optimal repurposing by decreasing the time and cost of drug discovery and development.

Machine learning & deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry

Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead optimization in drug discovery research, requires molecular representation. Previous reports have demonstrated that machine learning (ML) and deep learning (DL) have substantial implications in virtual screening, peptide synthesis, drug ADMET screening and biomarker discovery. These strategies can increase the positive outcomes in the drug discovery process without false-positive rates and can be achieved in a cost-effective way with a minimum duration of time by high-quality data acquisition. This review substantially discusses the recent updates in AI tools as cheminformatics application in medicinal chemistry for the data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry while improving small-molecule bioactivities and properties.

A Deep-Learning Proteomic-Scale Approach for Drug Design

Computational approaches have accelerated novel therapeutic discovery in recent decades. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget therapeutic discovery, repurposing, and design aims to improve their efficacy and safety by employing a holistic approach that computes interaction signatures between every drug/compound and a large library of non-redundant protein structures corresponding to the human proteome fold space. These signatures are compared and analyzed to determine if a given drug/compound is efficacious and safe for a given indication/disease. In this study, we used a deep learning-based autoencoder to first reduce the dimensionality of CANDO-computed drug-proteome interaction signatures. We then employed a reduced conditional variational autoencoder to generate novel drug-like compounds when given a target encoded "objective" signature. Using this approach, we designed compounds to recreate the interaction signatures for twenty approved and experimental drugs and showed that 16/20 designed compounds were predicted to be significantly (p-value ≤ 0.05) more behaviorally similar relative to all corresponding controls, and 20/20 were predicted to be more behaviorally similar relative to a random control. We further observed that redesigns of objectives developed via rational drug design performed significantly better than those derived from natural sources (p-value ≤ 0.05), suggesting that the model learned an abstraction of rational drug design. We also show that the designed compounds are structurally diverse and synthetically feasible when compared to their respective objective drugs despite consistently high predicted behavioral similarity. Finally, we generated new designs that enhanced thirteen drugs/compounds associated with non-small cell lung cancer and anti-aging properties using their predicted proteomic interaction signatures. his study represents a significant step forward in automating holistic therapeutic design with machine learning, enabling the rapid generation of novel, effective, and safe drug leads for any indication.

Advances in the treatment of Chagas disease: Promising new drugs, plants and targets

Chagas disease, caused by Trypanosoma cruzi, is treated with only two drugs; benznidazole and nifurtimox. These drugs have some disadvantages, including their efficacy only in the acute or early infection phases, adverse effects during their use, and the resistance that the parasite has developed to their activity. Therefore, it is necessary to identify new, safe and effective therapeutic alternatives to treat Chagas disease, though governments and the pharmaceutical industry have shown a lack of interest in contributing to this solution. Institutions and research groups on the other hand have worked on some strategies that can help to address the problem. Some of these include the modification of conventional drug dosages, drug repurposing, and combined therapy. Plants and derived compounds with antiparasitic effects have also been studied, taking advantage of traditional medicinal knowledge. Others have studied the parasite to identify essential genes that can be used as therapeutic targets to design new, targeted drugs. Some of these studies have generated promising results, but few reach clinical phase studies. Institutions and research groups should be encouraged to unify efforts and cover all aspects of drug development according to resources and knowledge availability. In the end, this exchange of knowledge would lead to the development of new therapeutic alternatives to treat Chagas disease and benefit the populations it affects.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

Accurate Prediction of Inhibitor Binding to HIV-1 Protease Using CANDOCK

The human immunodeficiency virus 1 (HIV-1) protease is an important target for treating HIV infection. Our goal was to benchmark a novel molecular docking protocol and determine its effectiveness as a therapeutic repurposing tool by predicting inhibitor potency to this target. To accomplish this, we predicted the relative binding scores of various inhibitors of the protease using CANDOCK, a hierarchical fragment-based docking protocol with a knowledge-based scoring function. We first used a set of 30 HIV-1 protease complexes as an initial benchmark to optimize the parameters for CANDOCK. We then compared the results from CANDOCK to two other popular molecular docking protocols Autodock Vina and Smina. Our results showed that CANDOCK is superior to both of these protocols in terms of correlating predicted binding scores to experimental binding affinities with a Pearson coefficient of 0.62 compared to 0.48 and 0.49 for Vina and Smina, respectively. We further leveraged the Database of Useful Decoys: Enhanced (DUD-E) HIV protease set to ascertain the effectiveness of each protocol in discriminating active versus decoy ligands for proteases. CANDOCK again displayed better efficacy over the other commonly used molecular docking protocols with area under the receiver operating characteristic curve (AUROC) of 0.94 compared to 0.71 and 0.74 for Vina and Smina. These findings support the utility of CANDOCK to help discover novel therapeutics that effectively inhibit HIV-1 and possibly other retroviral proteases.

Recent advances in multitarget-directed ligands targeting G-protein-coupled receptors

Mounting evidence indicates that single-target drugs might be inadequate to achieve satisfactory therapeutic effects on complex diseases. Recently, increasing attention has been paid to developing drugs that can manipulate multiple targets to generate beneficial effects through potential synergy. G-protein-coupled receptors (GPCRs) become desirable targets for developing multitarget-directed ligands (MTDLs) because of their crucial roles in the pathophysiology of various human diseases and the accessibility of druggable sites at the cell surface. Herein, we review the most recent advances in the development of GPCR-targeted MTDLs in treating complex diseases, and discuss their potential therapeutic strategies to reveal current trends and shed insights into the utility of GPCR-targeted MTDLs for future drug design and development.

An integrated drug repurposing strategy for the rapid identification of potential SARS-CoV-2 viral inhibitors

The Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2). The virus has rapidly spread in humans, causing the ongoing Coronavirus pandemic. Recent studies have shown that, similarly to SARS-CoV, SARS-CoV-2 utilises the Spike glycoprotein on the envelope to recognise and bind the human receptor ACE2. This event initiates the fusion of viral and host cell membranes and then the viral entry into the host cell. Despite several ongoing clinical studies, there are currently no approved vaccines or drugs that specifically target SARS-CoV-2. Until an effective vaccine is available, repurposing FDA approved drugs could significantly shorten the time and reduce the cost compared to de novo drug discovery. In this study we attempted to overcome the limitation of in silico virtual screening by applying a robust in silico drug repurposing strategy. We combined and integrated docking simulations, with molecular dynamics (MD), Supervised MD (SuMD) and Steered MD (SMD) simulations to identify a Spike protein - ACE2 interaction inhibitor. Our data showed that Simeprevir and Lumacaftor bind the receptor-binding domain of the Spike protein with high affinity and prevent ACE2 interaction.

Prospects of multitarget drug designing strategies by linking molecular docking and molecular dynamics to explore the protein-ligand recognition process

The designing of drugs that can simultaneously affect different protein targets is one novel and promising way to treat complex diseases. Multitarget drugs act on multiple protein receptors each implicated in the same disease state, and may be considered to be more beneficial than conventional drug therapies. For example, these drugs can have improved therapeutic potency due to synergistic effects on multiple targets, as well as improved safety and resistance profiles due to the combined regulation of potential primary therapeutic targets and compensatory elements and lower dosage typically required. This review analyzes in-silico methods that facilitate multitarget drug design that facilitate the discovery and development of novel therapeutic agents. Here presented is a summary of the progress in structure-based drug discovery techniques that study the process of molecular recognition of targets and ligands, moving from static molecular docking to improved molecular dynamics approaches in multitarget drug design, and the advantages and limitations of each.

CANDOCK: Chemical Atomic Network-Based Hierarchical Flexible Docking Algorithm Using Generalized Statistical Potentials

Small-molecule docking has proven to be invaluable for drug design and discovery. However, existing docking methods have several limitations such as improper treatment of the interactions of essential components in the chemical environment of the binding pocket (e.g., cofactors, metal ions, etc.), incomplete sampling of chemically relevant ligand conformational space, and the inability to consistently correlate docking scores of the best binding pose with experimental binding affinities. We present CANDOCK, a novel docking algorithm, that utilizes a hierarchical approach to reconstruct ligands from an atomic grid using graph theory and generalized statistical potential functions to sample biologically relevant ligand conformations. Our algorithm accounts for protein flexibility, solvent, metal ions, and cofactor interactions in the binding pocket that are traditionally ignored by current methods. We evaluate the algorithm on the PDBbind, Astex, and PINC proteins to show its ability to reproduce the binding mode of the ligands that is independent of the initial ligand conformation in these benchmarks. Finally, we identify the best selector and ranker potential functions such that the statistical score of the best selected docked pose correlates with the experimental binding affinities of the ligands for any given protein target. Our results indicate that CANDOCK is a generalized flexible docking method that addresses several limitations of current docking methods by considering all interactions in the chemical environment of a binding pocket for correlating the best-docked pose with biological activity. CANDOCK along with all structures and scripts used for benchmarking is available at https://github.com/chopralab/candock_benchmark.

Anticandidal agent for multiple targets: the next paradigm in the discovery of proficient therapeutics/overcoming drug resistance

Candida albicans is a prominent human fungal pathogen. Current treatments are suffering a massive gap due to emerging resistance against available antifungals. Therefore, there is an ardent need for novel antifungal candidates that essentially have more than one target, as most antifungal repertoires are single-target drugs. Exploration of multiple-drug targeting in antifungal therapeutics is still pending. An extensive literature survey was performed to categorize and comprehend relevant studies and the current therapeutic scenario that led researchers to preferentially consider multitarget drug-based Candida infection therapy. With this article, we identified and compiled a few potent antifungal compounds that are directed toward multiple virulent targets in C. albicans. Such compound(s) provide an optimistic platform of multiple targeting and could leave a substantial impact on the development of effective antifungals.

Fingerprinting CANDO: Increased Accuracy with Structure- and Ligand-Based Shotgun Drug Repurposing

We have upgraded our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun drug repurposing by including ligand-based, data fusion, and decision tree pipelines. The goal of shotgun drug repurposing is to screen and rank every existing human use drug or compound for every disease/indication. The first version of CANDO implemented a structure-based pipeline that modeled interactions between compounds and proteins on a large scale, generating compound-proteome interaction signatures used to infer the similarity of drug behavior; the new pipelines accomplish this by incorporating molecular fingerprints and the Tanimoto coefficient. We obtain improved benchmarking performance with the new pipelines across all three evaluation metrics used: average indication accuracy, pairwise accuracy, and coverage. The best performing pipeline achieves an average indication accuracy of 19.0% at the top10 cutoff, compared to 11.7% for v1, and 2.2% for a random control. Our results demonstrate that the CANDO drug recovery accuracy is substantially improved by integrating multiple pipelines, thereby enhancing our ability to generate putative therapeutic repurposing candidates, and increasing drug discovery efficiency.

Computational chemoproteomics to understand the role of selected psychoactives in treating mental health indications

We have developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform to infer homology of drug behaviour at a proteomic level by constructing and analysing structural compound-proteome interaction signatures of 3,733 compounds with 48,278 proteins in a shotgun manner. We applied the CANDO platform to predict putative therapeutic properties of 428 psychoactive compounds that belong to the phenylethylamine, tryptamine, and cannabinoid chemical classes for treating mental health indications. Our findings indicate that these 428 psychoactives are among the top-ranked predictions for a significant fraction of mental health indications, demonstrating a significant preference for treating such indications over non-mental health indications, relative to randomized controls. Also, we analysed the use of specific tryptamines for the treatment of sleeping disorders, bupropion for substance abuse disorders, and cannabinoids for epilepsy. Our innovative use of the CANDO platform may guide the identification and development of novel therapies for mental health indications and provide an understanding of their causal basis on a detailed mechanistic level. These predictions can be used to provide new leads for preclinical drug development for mental health and other neurological disorders.

The stilbene derivatives, nucleosides, and nucleosides modified by stilbene derivatives

In this short review, including 187 references, the issues of biological activity of stilbene derivatives and nucleosides and the biological and medicinal potential of fusion of these two classes are discussed. The stilbenes, especially the stilbenoids, and nucleosides are both biologically active. Hybrids formed from binding of these compounds have not yet been broadly studied. However, those that have been investigated exhibit desirable medicinal properties. The review is divided in such parts: I. Derivative of stilbene (biomedical investigations, biological activities in cells, enzymes and hazard), parts II. naturally occurred nucleoside and its derivatives: uridine, thymidine and 5-methyluridine, cytidine, adenosine, guanosine and part III. hybrid molecules- drugs and hybrid molecules- nucleoside - stilbene and its derivative.

Exploration of interaction scoring criteria in the CANDO platform

OBJECTIVE

Ascertain the optimal interaction scoring criteria for the Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun drug repurposing to improve benchmarking performance, thereby enabling more accurate prediction of novel therapeutic drug-indication pairs.

RESULTS

We have investigated and enhanced the interaction scoring criteria in the bioinformatic docking protocol in the newest version of our platform (v1.5), with the best performing interaction scoring criterion yielding increased benchmarking accuracies from 11.7% in v1 to 12.8% in v1.5 at the top10 cutoff (the most stringent one) and correspondingly from 24.9 to 31.2% at the top100 cutoff.

Rational Design of Multitarget-Directed Ligands: Strategies and Emerging Paradigms

Due to the complexity of multifactorial diseases, single-target drugs do not always exhibit satisfactory efficacy. Recently, increasing evidence indicates that simultaneous modulation of multiple targets may improve both therapeutic safety and efficacy, compared with single-target drugs. However, few multitarget drugs are on market or in clinical trials, despite the best efforts of medicinal chemists. This article discusses the systematic establishment of target combination, lead generation, and optimization of multitarget-directed ligands (MTDLs). Moreover, we analyze some MTDLs research cases for several complex diseases in recent years and the physicochemical properties of 117 clinical multitarget drugs, with the aim to reveal the trends and insights of the potential use of MTDLs.

Identifying Protein Features Responsible for Improved Drug Repurposing Accuracies Using the CANDO Platform: Implications for Drug Design

Drug repurposing is a valuable tool for combating the slowing rates of novel therapeutic discovery. The Computational Analysis of Novel Drug Opportunities (CANDO) platform performs shotgun repurposing of 2030 indications/diseases using 3733 drugs/compounds to predict interactions with 46,784 proteins and relating them via proteomic interaction signatures. The accuracy is calculated by comparing interaction similarities of drugs approved for the same indications. We performed a unique subset analysis by breaking down the full protein library into smaller subsets and then recombining the best performing subsets into larger supersets. Up to 14% improvement in accuracy is seen upon benchmarking the supersets, representing a 100⁻1000-fold reduction in the number of proteins considered relative to the full library. Further analysis revealed that libraries comprised of proteins with more equitably diverse ligand interactions are important for describing compound behavior. Using one of these libraries to generate putative drug candidates against malaria, tuberculosis, and large cell carcinoma results in more drugs that could be validated in the biomedical literature compared to using those suggested by the full protein library. Our work elucidates the role of particular protein subsets and corresponding ligand interactions that play a role in drug repurposing, with implications for drug design and machine learning approaches to improve the CANDO platform.

Implication of B23/NPM1 in Viral Infections, Potential Uses of B23/NPM1 Inhibitors as Antiviral Therapy

BACKGROUND

B23/nucleophosmin (B23/NPM1) is an abundant multifunctional protein mainly located in the nucleolus but constantly shuttling between the nucleus and cytosol. As a consequence of its constitutive expression, intracellular dynamics and binding capacities, B23/NPM1 interacts with multiple cellular factors in different cellular compartments, but also with viral proteins from both DNA and RNA viruses. B23/NPM1 influences overall viral replication of viruses like HIV, HBV, HCV, HDV and HPV by playing functional roles in different stages of viral replication including nuclear import, viral genome transcription and assembly, as well as final particle formation. Of note, some virus modify the subcellular localization, stability and/or increases B23/NPM1 expression levels on target cells, probably to foster B23/NPM1 functions in their own replicative cycle.

RESULTS

This review summarizes current knowledge concerning the interaction of B23/NPM1 with several viral proteins during relevant human infections. The opportunities and challenges of targeting this well-conserved host protein as a potentially new broad antiviral treatment are discussed in detail. Importantly, although initially conceived to treat cancer, a handful of B23/NPM1 inhibitors are currently available to test on viral infection models.

CONCLUSION

As B23/NPM1 partakes in key steps of viral replication and some viral infections remain as unsolved medical needs, an appealing idea may be the expedite evaluation of B23/NPM1 inhibitors in viral infections. Furthermore, worth to be addressed is if the up-regulation of B23/NPM1 protein levels that follows persistent viral infections may be instrumental to the malignant transformation induced by virus like HBV and HCV.

Steroids-specific target library for steroids target prediction

Steroids exist universally and play critical roles in various biological processes. Identifying potential targets of steroids is of great significance in studying their physiological and biochemical activities, the side effects and for drug repurposing. Herein, aiming at more precise steroids targets prediction, a steroids-specific target library integrating 3325 PDB or homology modeling structures categorized into 196 proteins was built by considering chemical similarity from DrugBank and biological processes from KEGG. The main properties of this library include: (1) It was manually prepared and checked to eliminate mistakes. (2) The library enriched the possible steroids targets and could decrease the false positives of structure-based target screening for steroids. (3) The ranking by protein name instead of PDB ID could make the screening more efficiency and precise. (4) Protein flexibility was taken into account partially by the different active conformations through the structural redundancy of each category of protein, which leads to more accurate prediction. The case studies of glycocholic acid and 24-epibrassinolide proved its powerful predictive accuracy. In summary, our strategy to build the steroids-specific protein library for steroids target prediction is a promising approach and it provides a novel idea for the target prediction of small molecules.

Molecular docking and in silico studies of the physicochemical properties of potential inhibitors for the phosphotransferase system of Streptococcus mutans

This study identified potential inhibitory compounds of the phosphoenolpyruvate-sugar. Phosphotransferase system of S. mutans, specifically enzyme II mannose transporter (EII^Man) in its subunits IIA, IIB and IIC by means of a selection protocol and in silico molecular analysis. Intervening the phosphotransferase system would compromise the physiological behavior and the pathogenic expression of S. mutans, and possibly other acidogenic bacteria that use phosphotransferases in their metabolism-making the phosphotransferase system a therapeutic target for the selective control of acidogenic microorganisms in caries control. Several computational techniques were used to evaluate molecular, physicochemical, and toxicological aspects of various compounds. Molecular docking was used to calculate the binding potential (ΔG) between receptor protein subunits and more than 836,000 different chemical compounds from the ZINC database. Physicochemical parameters related to the compounds' pharmacokinetic and pharmacodynamic indicators were evaluated, including absorption, distribution, metabolism, excretion, and toxicity (ADMET), and chemical analysis characterized the compounds structures. Thirteen compounds with EII binding potential of the phosphotransferase system of S. mutans and favorable ADMET properties were identified. Six spirooxindoles and three pyrrolidones stand out from the found compounds; unique structural characteristics of spirooxindoles and pyrrolidones associated with various reported biological activities like anti-microbial, antiinflammatory, anticancer, nootropic, neuroprotective and antiepileptic effects, among other pharmacological effects with surprising differences in terms of mechanisms of action. Following studies will provide more evidence of the action of these compounds on the phosphotransferase system of S. mutans, and its possible applications.

Computational Approaches Towards Kinases as Attractive Targets for Anticancer Drug Discovery and Development

BACKGROUND

One of the major goals of computational chemists is to determine and develop the pathways for anticancer drug discovery and development. In recent past, high performance computing systems elicited the desired results with little or no side effects. The aim of the current review is to evaluate the role of computational chemistry in ascertaining kinases as attractive targets for anticancer drug discovery and development.

METHODS

Research related to computational studies in the field of anticancer drug development is reviewed. Extensive literature on achievements of theorists in this regard has been compiled and presented with special emphasis on kinases being the attractive anticancer drug targets.

RESULTS

Different approaches to facilitate anticancer drug discovery include determination of actual targets, multi-targeted drug discovery, ligand-protein inverse docking, virtual screening of drug like compounds, formation of di-nuclear analogs of drugs, drug specific nano-carrier design, kinetic and trapping studies in drug design, multi-target QSAR (Quantitative Structure Activity Relationship) model, targeted co-delivery of anticancer drug and siRNA, formation of stable inclusion complex, determination of mechanism of drug resistance, and designing drug like libraries for the prediction of drug-like compounds. Protein kinases have gained enough popularity as attractive targets for anticancer drugs. These kinases are responsible for uncontrolled and deregulated differentiation, proliferation, and cell signaling of the malignant cells which result in cancer.

CONCLUSION

Interest in developing drugs through computational methods is a growing trend, which saves equally the cost and time. Kinases are the most popular targets among the other for anticancer drugs which demand attention. 3D-QSAR modelling, molecular docking, and other computational approaches have not only identified the target-inhibitor binding interactions for better anticancer drug discovery but are also designing and predicting new inhibitors, which serve as lead for the synthetic preparation of drugs. In light of computational studies made so far in this field, the current review highlights the importance of kinases as attractive targets for anticancer drug discovery and development.

Computational Approaches in Multitarget Drug Discovery

Current therapeutic strategies entail identifying and characterizing a single protein receptor whose inhibition is likely to result in the successful treatment of a disease of interest, and testing experimentally large libraries of small molecule compounds "in vitro" and "in vivo" to identify promising inhibitors in model systems and determine if the findings are extensible to humans. This highly complex process is largely based on tests, errors, risk, time, and intensive costs. The virtual computational study of compounds simulates situations predicting possible drug linkages with multiple protein target atomic structures, taking into account the dynamic protein inhibitor, and can help identify inhibitors efficiently, particularly for complex drug-resistant diseases. Some discussions will relate to the potential benefits of this approach, using HIV-1 and Plasmodium falciparum infections as examples. Some authors have proposed a virtual drug discovery that not only identifies efficient inhibitors but also helps to minimize side effects and toxicity, thus increasing the likelihood of successful therapies. This chapter discusses concepts and research of bioactive multitargets related to toxicology.

Quantitative structure-activity relationship of anti-HIV integrase and reverse transcriptase inhibitors using norm indexes

The development of new and safe anti-human immunodeficiency virus (anti-HIV) drugs has been an urgent task for medical research recently. Herein, based on the norm-index descriptors proposed in this work and previous works, a couple of models were developed for investigating the quantitative structure-activity/toxicity relationship (QSAR/QSTR) of dual-target anti-HIV integrase (IN) and reverse transcriptase (RT) inhibitors. The validation results proved that the developed models were stable and reliable, both in statistical quality and predictive capacity. Moreover, potential dual-target inhibitors with high activity and low toxicity were deduced from the developed models; molecular docking results indicated that these inhibitors could interact with some important residues of HIV IN and RT through H-bonding. Accordingly, the norm indexes descriptors proposed by this work might be helpful for the research and development of dual-target anti-HIV drugs.

Exploring Polypharmacology in Drug Discovery and Repurposing Using the CANDO Platform

BACKGROUND

Traditional drug discovery approaches focus on a limited set of target molecules for treatment against specific indications/diseases. However, drug absorption, dispersion, metabolism, and excretion (ADME) involve interactions with multiple protein systems. Drugs approved for particular indication(s) may be repurposed as novel therapeutics for others. The severely declining rate of discovery and increasing costs of new drugs illustrate the limitations of the traditional reductionist paradigm in drug discovery.

METHODS

We developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform based on a hypothesis that drugs function by interacting with multiple protein targets to create a molecular interaction signature that can be exploited for therapeutic repurposing and discovery. We compiled a library of compounds that are human ingestible with minimal side effects, followed by an 'all-compounds' vs 'all-proteins' fragment-based multitarget docking with dynamics screen to construct compound-proteome interaction matrices that were then analyzed to determine similarity of drug behavior. The proteomic signature similarity of drugs is then ranked to make putative drug predictions for all indications in a shotgun manner.

RESULTS

We have previously applied this platform with success in both retrospective benchmarking and prospective validation, and to understand the effect of druggable protein classes on repurposing accuracy. Here we use the CANDO platform to analyze and determine the contribution of multitargeting (polypharmacology) to drug repurposing benchmarking accuracy. Taken together with the previous work, our results indicate that a large number of protein structures with diverse fold space and a specific polypharmacological interactome is necessary for accurate drug predictions using our proteomic and evolutionary drug discovery and repurposing platform.

CONCLUSION

These results have implications for future drug development and repurposing in the context of polypharmacology.

A Systematic Review of Computational Drug Discovery, Development, and Repurposing for Ebola Virus Disease Treatment

Ebola virus disease (EVD) is a deadly global public health threat, with no currently approved treatments. Traditional drug discovery and development is too expensive and inefficient to react quickly to the threat. We review published research studies that utilize computational approaches to find or develop drugs that target the Ebola virus and synthesize its results. A variety of hypothesized and/or novel treatments are reported to have potential anti-Ebola activity. Approaches that utilize multi-targeting/polypharmacology have the most promise in treating EVD.

Dual or multi-targeting inhibitors: The next generation anticancer agents

Dual-targeting/Multi-targeting of oncoproteins by a single drug molecule represents an efficient, logical and alternative approach to drug combinations. An increasing interest in this approach is indicated by a steady upsurge in the number of articles on targeting dual/multi proteins published in the last 5 years. Combining different inhibitors that destiny specific single target is the standard treatment for cancer. A new generation of dual or multi-targeting drugs is emerging, where a single chemical entity can act on multiple molecular targets. Dual/Multi-targeting agents are beneficial for solving limited efficiencies, poor safety and resistant profiles of an individual target. Designing dual/multi-target inhibitors with predefined biological profiles present a challenge. The latest advances in bioinformatic tools and the availability of detailed structural information of target proteins have shown a way of discovering multi-targeting molecules. This neoteric artifice that amalgamates the molecular docking of small molecules with protein-based common pharmacophore to design multi-targeting inhibitors is gaining great importance in anticancer drug discovery. Current review focus on the discoveries of dual targeting agents in cancer therapy using rational, computational, proteomic, bioinformatics and polypharmacological approach that enables the discovery and rational design of effective and safe multi-target anticancer agents.

Teriflunomide restores 5-azacytidine sensitivity via activation of pyrimidine salvage in 5-azacytidine-resistant leukemia cells

Previous studies showed that downregulation of pyrimidine salvage underlies resistance against 5-azacytidine (AZA), indicating an important role for de novo pyrimidine synthesis in AZA resistance. Because de novo pyrimidine synthesis is inhibited by the immunomodulator teriflunomide and its pro-drug leflunomide, we examined the effect of combined treatment with AZA and teriflunomide on AZA resistance to develop a novel strategy to cancel and prevent AZA resistance. Teriflunomide markedly inhibited the growth of AZA-resistant human leukemia cell lines (R-U937 and R-HL-60) in comparison with their AZA-sensitive counterparts (U937 and HL-60). In the presence of a non-toxic concentration of teriflunomide (1 μM), AZA induced apoptosis in AZA-resistant cells and leukemia cells from AZA-resistant patients. AZA acted as a DNA methyltransferase 3A inhibitor in AZA-resistant cells in the presence of 1 μM teriflunomide. Although AZA-sensitive cells acquired AZA resistance after continuous treatment with AZA for 42 days, the growth of AZA-sensitive cells continuously treated with the combination of AZA and teriflunomide was significantly inhibited in the presence of AZA, demonstrating that the combined treatment prevented AZA resistance. These results suggest that combined treatment with AZA and teriflunomide can be a novel strategy to overcome AZA resistance.

Design and computational support for the binding stability of a new CCR5/CXCR4 dual tropic inhibitor: Computational design of a CCR5/CXCR4 drug

The human immunodeficiency virus (HIV) infects healthy human cells by binding to the glycoprotein cluster of differentiation 4 receptors on the surface of helper T-cells, along with either of two chemokine receptors, CC chemokine receptor type 5 (CCR5) or C-X-C chemokine receptor type 4 (CXCR4). Recently, a pyrazolo-piperdine ligand was synthesized and the corresponding biological data showed good binding to both chemokine receptors, effectively blocking HIV-1 entry. Here, we exhaustively assess the atomistic binding interactions of this compound with both CCR5 and CXCR4, and we find that binding is driven by π-stacking interactions between aromatic rings on the ligand and receptor residues, as well as electrostatic interactions involving the protonated piperidine nitrogen. However, these favorable binding interactions were partially offset by unfavorable desolvation of active site glutamates and aspartates, prompting our proposal of a new, synthetically-accessible derivative designed to increase the electrostatic interactions without compromising the π-stacking features.

The scoring bias in reverse docking and the score normalization strategy to improve success rate of target fishing

Target fishing often relies on the use of reverse docking to identify potential target proteins of ligands from protein database. The limitation of reverse docking is the accuracy of current scoring funtions used to distinguish true target from non-target proteins. Many contemporary scoring functions are designed for the virtual screening of small molecules without special optimization for reverse docking, which would be easily influenced by the properties of protein pockets, resulting in scoring bias to the proteins with certain properties. This bias would cause lots of false positives in reverse docking, interferring the identification of true targets. In this paper, we have conducted a large-scale reverse docking (5000 molecules to 100 proteins) to study the scoring bias in reverse docking by DOCK, Glide, and AutoDock Vina. And we found that there were actually some frequency hits, namely interference proteins in all three docking procedures. After analyzing the differences of pocket properties between these interference proteins and the others, we speculated that the interference proteins have larger contact area (related to the size and shape of protein pockets) with ligands (for all three docking programs) or higher hydrophobicity (for Glide), which could be the causes of scoring bias. Then we applied the score normalization method to eliminate this scoring bias, which was effective to make docking score more balanced between different proteins in the reverse docking of benchmark dataset. Later, the Astex Diver Set was utilized to validate the effect of score normalization on actual cases of reverse docking, showing that the accuracy of target prediction significantly increased by 21.5% in the reverse docking by Glide after score normalization, though there was no obvious change in the reverse docking by DOCK and AutoDock Vina. Our results demonstrate the effectiveness of score normalization to eliminate the scoring bias and improve the accuracy of target prediction in reverse docking. Moreover, the properties of protein pockets causing scoring bias to certain proteins we found here can provide the theory basis to further optimize the scoring functions of docking programs for future research.

Combating Ebola with Repurposed Therapeutics Using the CANDO Platform

Ebola virus disease (EVD) is extremely virulent with an estimated mortality rate of up to 90%. However, the state-of-the-art treatment for EVD is limited to quarantine and supportive care. The 2014 Ebola epidemic in West Africa, the largest in history, is believed to have caused more than 11,000 fatalities. The countries worst affected are also among the poorest in the world. Given the complexities, time, and resources required for a novel drug development, finding efficient drug discovery pathways is going to be crucial in the fight against future outbreaks. We have developed a Computational Analysis of Novel Drug Opportunities (CANDO) platform based on the hypothesis that drugs function by interacting with multiple protein targets to create a molecular interaction signature that can be exploited for rapid therapeutic repurposing and discovery. We used the CANDO platform to identify and rank FDA-approved drug candidates that bind and inhibit all proteins encoded by the genomes of five different Ebola virus strains. Top ranking drug candidates for EVD treatment generated by CANDO were compared to in vitro screening studies against Ebola virus-like particles (VLPs) by Kouznetsova et al. and genetically engineered Ebola virus and cell viability studies by Johansen et al. to identify drug overlaps between the in virtuale and in vitro studies as putative treatments for future EVD outbreaks. Our results indicate that integrating computational docking predictions on a proteomic scale with results from in vitro screening studies may be used to select and prioritize compounds for further in vivo and clinical testing. This approach will significantly reduce the lead time, risk, cost, and resources required to determine efficacious therapies against future EVD outbreaks.

Mycobacterium Cytidylate Kinase Appears to Be an Undruggable Target

New and improved drugs against tuberculosis are urgently needed as multi-drug-resistant forms of the disease become more prevalent. Mycobacterium tuberculosis cytidylate kinase is an attractive target for screening due to its essentiality and different substrate specificity to the human orthologue. However, we selected the Mycobacterium smegmatis cytidylate kinase for screening because of the availability of high-resolution X-ray crystallographic data defining its structure and the high likelihood of active site structural similarity to the M. tuberculosis orthologue. We report the development and implementation of a high-throughput luciferase-based activity assay and screening of 19,920 compounds derived from small-molecule libraries and an in silico screen predicting likely inhibitors of the cytidylate kinase enzyme. Hit validation included a counterscreen for luciferase inhibitors that would result in false positives in the initial screen. Results of this counterscreen ruled out all of the putative cytidylate kinase inhibitors identified in the initial screening, leaving no compounds as candidates for drug development. Although a negative result, this study indicates that this important drug target may in fact be undruggable and serve as a warning for future investigations.

Pyrazolo-Piperidines Exhibit Dual Inhibition of CCR5/CXCR4 HIV Entry and Reverse Transcriptase

We report novel anti-HIV-1 agents with combined dual host-pathogen pharmacology. Lead compound 3, composed of a pyrazole-piperidine core, exhibits three concurrent mechanisms of action: (1) non-nucleoside reverse transcriptase inhibition, (2) CCR5-mediated M-tropic viral entry inhibition, and (3) CXCR4-based T-tropic viral entry inhibition that maintains native chemokine ligand binding. This discovery identifies important tool compounds for studying viral infectivity and prototype agents that block HIV-1 entry through dual chemokine receptor ligation.

CANDO and the infinite drug discovery frontier

The Computational Analysis of Novel Drug Opportunities (CANDO) platform (http://protinfo.org/cando) uses similarity of compound-proteome interaction signatures to infer homology of compound/drug behavior. We constructed interaction signatures for 3733 human ingestible compounds covering 48,278 protein structures mapping to 2030 indications based on basic science methodologies to predict and analyze protein structure, function, and interactions developed by us and others. Our signature comparison and ranking approach yielded benchmarking accuracies of 12-25% for 1439 indications with at least two approved compounds. We prospectively validated 49/82 'high value' predictions from nine studies covering seven indications, with comparable or better activity to existing drugs, which serve as novel repurposed therapeutics. Our approach may be generalized to compounds beyond those approved by the FDA, and can also consider mutations in protein structures to enable personalization. Our platform provides a holistic multiscale modeling framework of complex atomic, molecular, and physiological systems with broader applications in medicine and engineering.

Facing the challenges of structure-based target prediction by inverse virtual screening

Computational target prediction for bioactive compounds is a promising field in assessing off-target effects. Structure-based methods not only predict off-targets, but, simultaneously, binding modes, which are essential for understanding the mode of action and rationally designing selective compounds. Here, we highlight the current open challenges of computational target prediction methods based on protein structures and show why inverse screening rather than sequential pairwise protein-ligand docking methods are needed. A new inverse screening method based on triangle descriptors is introduced: iRAISE (inverse Rapid Index-based Screening Engine). A Scoring Cascade considering the reference ligand as well as the ligand and active site coverage is applied to overcome interprotein scoring noise of common protein-ligand scoring functions. Furthermore, a statistical evaluation of a score cutoff for each individual protein pocket is used. The ranking and binding mode prediction capabilities are evaluated on different datasets and compared to inverse docking and pharmacophore-based methods. On the Astex Diverse Set, iRAISE ranks more than 35% of the targets to the first position and predicts more than 80% of the binding modes with a root-mean-square deviation (RMSD) accuracy of <2.0 Å. With a median computing time of 5 s per protein, large amounts of protein structures can be screened rapidly. On a test set with 7915 protein structures and 117 query ligands, iRAISE predicts the first true positive in a ranked list among the top eight ranks (median), i.e., among 0.28% of the targets.