Improving small molecule virtual screening strategies for the next generation of therapeutics

Artificial intelligence: Machine learning approach for screening large database and drug discovery

Recent research in drug discovery dealing with many faces difficulties, including development of new drugs during disease outbreak and drug resistance due to rapidly accumulating mutations. Virtual screening is the most widely used method in computer aided drug discovery. It has a prominent ability in screening drug targets from large molecular databases. Recently, a number of web servers have developed for quickly screening publicly accessible chemical databases. In a nutshell, deep learning algorithms and artificial neural networks have modernised the field. Several drug discovery processes have used machine learning and deep learning algorithms, including peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modelling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Although there are presently a wide variety of data-driven AI/ML tools available, the majority of these tools have, up to this point, been developed in the context of non-communicable diseases like cancer, and a number of obstacles have prevented the translation of these tools to the discovery of treatments against infectious diseases. In this review various aspects of AI and ML in virtual screening of large databases were discussed. Here, with an emphasis on antivirals as well as other disease, offers a perspective on the advantages, drawbacks, and hazards of AI/ML techniques in the search for innovative treatments.

Hierarchical Virtual Screening of Potential New Antibiotics from Polyoxygenated Dibenzofurans against Staphylococcus aureus Strains

Staphylococcus aureus is a microorganism with high morbidity and mortality due to antibiotic-resistant strains, making the search for new therapeutic options urgent. In this context, computational drug design can facilitate the drug discovery process, optimizing time and resources. In this work, computational methods involving ligand- and structure-based virtual screening were employed to identify potential antibacterial agents against the S. aureus MRSA and VRSA strains. To achieve this goal, tetrahydroxybenzofuran, a promising antibacterial agent according to in vitro tests described in the literature, was adopted as the pivotal molecule and derivative molecules were considered to generate a pharmacophore model, which was used to perform virtual screening on the Pharmit platform. Through this result, twenty-four molecules were selected from the MolPort® database. Using the Tanimoto Index on the BindingDB web server, it was possible to select eighteen molecules with greater structural similarity in relation to commercial antibiotics (methicillin and oxacillin). Predictions of toxicological and pharmacokinetic properties (ADME/Tox) using the eighteen most similar molecules, showed that only three exhibited desired properties (LB255, LB320 and LB415). In the molecular docking study, the promising molecules LB255, LB320 and LB415 showed significant values in both molecular targets. LB320 presented better binding affinity to MRSA (-8.18 kcal/mol) and VRSA (-8.01 kcal/mol) targets. Through PASS web server, the three molecules, specially LB320, showed potential for antibacterial activity. Synthetic accessibility (SA) analysis performed on AMBIT and SwissADME web servers showed that LB255 and LB415 can be considered difficult to synthesize and LB320 is considered easy. In conclusion, the results suggest that these ligands, particularly LB320, may bind strongly to the studied targets and may have appropriate ADME/Tox properties in experimental studies.

A Hybrid Docking and Machine Learning Approach to Enhance the Performance of Virtual Screening Carried out on Protein-Protein Interfaces

The modulation of protein-protein interactions (PPIs) by small chemical compounds is challenging. PPIs play a critical role in most cellular processes and are involved in numerous disease pathways. As such, novel strategies that assist the design of PPI inhibitors are of major importance. We previously reported that the knowledge-based DLIGAND2 scoring tool was the best-rescoring function for improving receptor-based virtual screening (VS) performed with the Surflex docking engine applied to several PPI targets with experimentally known active and inactive compounds. Here, we extend our investigation by assessing the vs. potential of other types of scoring functions with an emphasis on docking-pose derived solvent accessible surface area (SASA) descriptors, with or without the use of machine learning (ML) classifiers. First, we explored rescoring strategies of Surflex-generated docking poses with five GOLD scoring functions (GoldScore, ChemScore, ASP, ChemPLP, ChemScore with Receptor Depth Scaling) and with consensus scoring. The top-ranked poses were post-processed to derive a set of protein and ligand SASA descriptors in the bound and unbound states, which were combined to derive descriptors of the docked protein-ligand complexes. Further, eight ML models (tree, bagged forest, random forest, Bayesian, support vector machine, logistic regression, neural network, and neural network with bagging) were trained using the derivatized SASA descriptors and validated on test sets. The results show that many SASA descriptors are better than Surflex and GOLD scoring functions in terms of overall performance and early recovery success on the used dataset. The ML models were superior to all scoring functions and rescoring approaches for most targets yielding up to a seven-fold increase in enrichment factors at 1% of the screened collections. In particular, the neural networks and random forest-based ML emerged as the best techniques for this PPI dataset, making them robust and attractive vs. tools for hit-finding efforts. The presented results suggest that exploring further docking-pose derived SASA descriptors could be valuable for structure-based virtual screening projects, and in the present case, to assist the rational design of small-molecule PPI inhibitors.

Identification of Small-Molecule Inhibitors of Fibroblast Growth Factor 23 Signaling via In Silico Hot Spot Prediction and Molecular Docking to α-Klotho

Fibroblast growth factor 23 (FGF23) is a therapeutic target for treating hereditary and acquired hypophosphatemic disorders, such as X-linked hypophosphatemic (XLH) rickets and tumor-induced osteomalacia (TIO), respectively. FGF23-induced hypophosphatemia is mediated by signaling through a ternary complex formed by FGF23, the FGF receptor (FGFR), and α-Klotho. Currently, disorders of excess FGF23 are treated with an FGF23-blocking antibody, burosumab. Small-molecule drugs that disrupt protein/protein interactions necessary for the ternary complex formation offer an alternative to disrupting FGF23 signaling. In this study, the FGF23:α-Klotho interface was targeted to identify small-molecule protein/protein interaction inhibitors since it was computationally predicted to have a large fraction of hot spots and two druggable residues on α-Klotho. We further identified Tyr433 on the KL1 domain of α-Klotho as a promising hot spot and α-Klotho as an appropriate drug-binding target at this interface. Subsequently, we performed in silico docking of ∼5.5 million compounds from the ZINC database to the interface region of α-Klotho from the ternary crystal structure. Following docking, 24 and 20 compounds were in the final list based on the lowest binding free energies to α-Klotho and the largest number of contacts with Tyr433, respectively. Five compounds were assessed experimentally by their FGF23-mediated extracellular signal-regulated kinase (ERK) activities in vitro, and two of these reduced activities significantly. Both these compounds were predicted to have favorable binding affinities to α-Klotho but not have a large number of contacts with the hot spot Tyr433. ZINC12409120 was found experimentally to disrupt FGF23:α-Klotho interaction to reduce FGF23-mediated ERK activities by 70% and have a half maximal inhibitory concentration (IC₅₀) of 5.0 ± 0.23 μM. Molecular dynamics (MD) simulations of the ZINC12409120:α-Klotho complex starting from in silico docking poses reveal that the ligand exhibits contacts with residues on the KL1 domain, the KL1-KL2 linker, and the KL2 domain of α-Klotho simultaneously, thereby possibly disrupting the regular function of α-Klotho and impeding FGF23:α-Klotho interaction. ZINC12409120 is a candidate for lead optimization.

Impact of Olive Oil Constituents on C-reactive Protein: In silico Evidence

Pain is a sensation a humans sense as a protective mechanism against physical injury. This sensation is closely related to inflammation. It ranges from mild to highly obnoxious. It is well-known that the levels of the inflammatory biomarker, C-reactive protein (CRP), increase manifold in acute inflammation and pain. Olive oil, known to have many phytochemicals, has been traditionally used to alleviate pain. Amongst major phenolic compounds in olive oil are oleuropein (OLE), hydroxytyrosol (HT), tyrosol, and oleocanthal. Whether the analgesic and anti-inflammatory properties in olive oil are due to any specific interections is not known. Therefore, this study aimed to elucidate the possible anti-inflammatory and anti-nociceptive properties in those major phenolic compounds by using molecular docking software MOE 2015, comparing the energy value and binding site of phenolic compounds to that of well-known synthetic non-steroidal anti-inflammatory drugs (NSAIDs) and phosphocholine. The docking experiment showed that all compounds could directly interact with CRP. Oleuropein had the most potent interaction with CRP (-7.7580), followed by indomethacin (-6.0775), oleocanthal (-5.5734), ibuprofen (-5.3857), phosphocholine (-4.3876), HT (-4.2782), and tyrosol (-4.2329). Interestingly, the present study found other phytochemicals in olive oil that can be exploited as potential, safe, and cost-effective lead compound(s) for analgesic and anti-inflammatory activity, as supported by its molecular docking data.

Identification of Novel Inhibitors Targeting SGK1 via Ensemble-Based Virtual Screening Method, Biological Evaluation and Molecular Dynamics Simulation

Serum and glucocorticoid-regulated kinase 1 (SGK1), as a serine threonine protein kinase of the AGC family, regulates different enzymes, transcription factors, ion channels, transporters, and cell proliferation and apoptosis. Inhibition of SGK1 is considered as a valuable approach for the treatment of various metabolic diseases. In this investigation, virtual screening methods, including pharmacophore models, Bayesian classifiers, and molecular docking, were combined to discover novel inhibitors of SGK1 from the database with 29,158 compounds. Then, the screened compounds were subjected to ADME/T, PAINS and drug-likeness analysis. Finally, 28 compounds with potential inhibition activity against SGK1 were selected for biological evaluation. The kinase inhibition activity test revealed that among these 28 hits, hit15 exhibited the highest inhibition activity against SGK1, which gave 44.79% inhibition rate at the concentration of 10 µM. In order to further investigate the interaction mechanism of hit15 and SGK1 at simulated physiological conditions, a molecular dynamics simulation was performed. The molecular dynamics simulation demonstrated that hit15 could bind to the active site of SGK1 and form stable interactions with key residues, such as Tyr178, ILE179, and VAL112. The binding free energy of the SGK1-hit15 was −48.90 kJ mol−1. Therefore, the identified hit15 with novel scaffold may be a promising lead compound for development of new SGK1 inhibitors for various diseases treatment.

Old targets, new strategy: Apigenin-7-O-β-d-(-6″-p-coumaroyl)-glucopyranoside prevents endothelial ferroptosis and alleviates intestinal ischemia-reperfusion injury through HO-1 and MAO-B inhibition

With the increasing morbidity and mortality, intestinal ischemia/reperfusion injury (IIRI) has attracted more and more attention, but there is no efficient therapeutics at present. Apigenin-7-O-β-D-(-6″-p-coumaroyl)-glucopyranoside (APG) is a new flavonoid glycoside isolated from Clematis tangutica that has strong antioxidant abilities in previous studies. However, the pharmacodynamic function and mechanism of APG on IIRI remain unknown. This study aimed to investigate the effects of APG on IIRI both in vivo and in vitro and identify the potential molecular mechanism. We found that APG could significantly improve intestinal edema and increase Chiu's score. MST analysis suggested that APG could specifically bind to heme oxygenase 1 (HO-1) and monoamine oxidase b (MAO-B). Simultaneously, APG could attenuate ROS generation and Fe²⁺ accumulation, maintain mitochondria function thus inhibit ferroptosis with a dose-dependent manner. Moreover, we used siRNA silencing technology to confirm that knocking down both HO-1 and MAO-B had a positive effect on intestine. In addition, we found the HO-1 and MAO-B inhibitors also could reduce endothelial cell loss and protect vascular endothelial after reperfusion. We demonstrate that APG plays a protective role on decreasing activation of HO-1 and MAO-B, attenuating IIRI-induced ROS generation and Fe²⁺ accumulation, maintaining mitochondria function thus inhibiting ferroptosis.

A feature transferring workflow between data-poor compounds in various tasks

Compound screening by in silico approaches has advantages in identifying high-activity leading compounds and can predict the safety of the drug. A key challenge is that the number of observations of drug activity and toxicity accumulation varies by target in different datasets, some of which are more understudied than others. Owing to an overall insufficiency and imbalance of drug data, it is hard to accurately predict drug activity and toxicity of multiple tasks by the existing models. To solve this problem, this paper proposed a two-stage transfer learning workflow to develop a novel prediction model, which can accurately predict drug activity and toxicity of the targets with insufficient observations. We built a balanced dataset based on the Tox21 dataset and developed a drug activity and toxicity prediction model based on Siamese networks and graph convolution to produce multitasking output. We also took advantage of transfer learning from data-rich targets to data-poor targets. We showed greater accuracy in predicting the activity and toxicity of compounds to targets with rich data and poor data. In Tox21, a relatively rich dataset, the prediction model accuracy for classification tasks was 0.877 AUROC. In the other five unbalanced datasets, we also found that transfer learning strategies brought the accuracy of models to a higher level in understudied targets. Our models can overcome the imbalance in target data and predict the compound activity and toxicity of understudied targets to help prioritize upcoming biological experiments.

Medicinal chemistry strategies towards the development of effective SARS-CoV-2 inhibitors

Novel therapies are urgently needed to improve global treatment of SARS-CoV-2 infection. Herein, we briefly provide a concise report on the medicinal chemistry strategies towards the development of effective SARS-CoV-2 inhibitors with representative examples in different strategies from the medicinal chemistry perspective.

Deep Learning in Drug Design: Protein-Ligand Binding Affinity Prediction

Computational drug design relies on the calculation of binding strength between two biological counterparts especially a chemical compound, i.e., a ligand, and a protein. Predicting the affinity of protein-ligand binding with reasonable accuracy is crucial for drug discovery, and enables the optimization of compounds to achieve better interaction with their target protein. In this paper, we propose a data-driven framework named DeepAtom to accurately predict the protein-ligand binding affinity. With 3D Convolutional Neural Network (3D-CNN) architecture, DeepAtom could automatically extract binding related atomic interaction patterns from the voxelized complex structure. Compared with the other CNN based approaches, our light-weight model design effectively improves the model representational capacity, even with the limited available training data. We carried out validation experiments on the PDBbind v.2016 benchmark and the independent Astex Diverse Set. We demonstrate that the less feature engineering dependent DeepAtom approach consistently outperforms the other baseline scoring methods. We also compile and propose a new benchmark dataset to further improve the model performances. With the new dataset as training input, DeepAtom achieves Pearson's R=0.83 and RMSE=1.23 pK units on the PDBbind v.2016 core set. The promising results demonstrate that DeepAtom models can be potentially adopted in computational drug development protocols such as molecular docking and virtual screening.

A specific inhibitor of ALDH1A3 regulates retinoic acid biosynthesis in glioma stem cells

Elevated aldehyde dehydrogenase (ALDH) activity correlates with poor outcome for many solid tumors as ALDHs may regulate cell proliferation and chemoresistance of cancer stem cells (CSCs). Accordingly, potent, and selective inhibitors of key ALDH enzymes may represent a novel CSC-directed treatment paradigm for ALDH+ cancer types. Of the many ALDH isoforms, we and others have implicated the elevated expression of ALDH1A3 in mesenchymal glioma stem cells (MES GSCs) as a target for the development of novel therapeutics. To this end, our structure of human ALDH1A3 combined with in silico modeling identifies a selective, active-site inhibitor of ALDH1A3. The lead compound, MCI-INI-3, is a selective competitive inhibitor of human ALDH1A3 and shows poor inhibitory effect on the structurally related isoform ALDH1A1. Mass spectrometry-based cellular thermal shift analysis reveals that ALDH1A3 is the primary binding protein for MCI-INI-3 in MES GSC lysates. The inhibitory effect of MCI-INI-3 on retinoic acid biosynthesis is comparable with that of ALDH1A3 knockout, suggesting that effective inhibition of ALDH1A3 is achieved with MCI-INI-3. Further development is warranted to characterize the role of ALDH1A3 and retinoic acid biosynthesis in glioma stem cell growth and differentiation.

Fine tuning for success in structure-based virtual screening

Structure-based virtual screening plays a significant role in drug-discovery. The method virtually docks millions of compounds from corporate or public libraries into a binding site of a disease-related protein structure, allowing for the selection of a small list of potential ligands for experimental testing. Many algorithms are available for docking and assessing the affinity of compounds for a targeted protein site. The performance of affinity estimation calculations is highly dependent on the size and nature of the site, therefore a rationale for selecting the best protocol is required. To address this issue, we have developed an automated calibration process, implemented in a Knime workflow. It consists of four steps: preparation of a protein test set with structures and models of the target, preparation of a compound test set with target-related ligands and decoys, automatic test of 24 scoring/rescoring protocols for each target structure and model, and graphical display of results. The automation of the process combined with execution on high performance computing resources greatly reduces the duration of the calibration phase, and the test of many combinations of algorithms on various target conformations results in a rational and optimal choice of the best protocol. Here, we present this tool and exemplify its application in setting-up an optimal protocol for SBVS against Retinoid X Receptor alpha.

Discovery of novel tubulin inhibitors targeting taxanes site by virtual screening, molecular dynamic simulation, and biological evaluation

Microtubules play crucial role in process of mitosis and cell proliferation, which have been considered as attractive drug targets for anticancer therapy. The aim of this study was to discover novel and chemically diverse tubulin inhibitors for treatment of cancer. In this investigation, the multilayer virtual screening methods, including common feature pharmacophore model, structure-based pharmacophore model and molecular docking, were developed to screen BioDiversity database with 30,000 compounds. A total of 102 compounds were obtained by the virtual screening, and further filtered by diverse chemical clusters with desired properties and PAINS analysis. Finally, 50 compounds were selected and submitted to the biological evaluation. Among these hits, hits 8 and 30 with novel scaffolds displayed stronger antiproliferative activity on four human tumor cells including Hela, A549, MCF-7, and HepG2. Moreover, the two hits were subsequently submitted to molecular dynamic simulations of 90 ns with the aim of exploring the stability of ligand-protein interactions into the binding pocket, and further probing the mechanism of the interaction between tubulin and hits. The molecular dynamic simulation results revealed there had stronger interactions between tubulin and hits in equilibrium state. Therefore, the hits 8 and 30 have been well characterized as lead compounds for developing new tubulin inhibitors with potential anticancer activity.

Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.

Use of the Complementarity Principle in Docking Procedures: A New Approach for Evaluating the Correctness of Binding Poses

Even though the first docking procedures were developed almost 40 years ago, they are still under intense development, alongside with their validation. In this article, we are proposing the use of the quantum free-orbital AlteQ method in evaluating the correctness of ligand binding poses and their ranking. The AlteQ method calculates the electron density in the interspace between the ligand and the receptor, and since their interactions follow the maximum complementarity principle, an equation can be obtained, which describes these interactions. In this way, the AlteQ method evaluates the quality of contacts between the ligand and the receptor, bypasses the drawbacks of using ligand RMSD as a measure of docking quality, and can be considered as an improvement of the “fraction of recovered ligand–receptor contacts” method. Free Windows and Linux versions of the AlteQ program for assessing complementarity between the ligand and the receptor are available for download at www.chemosophia.com.

Tripeptide IRW Upregulates NAMPT Protein Levels in Cells and Obese C57BL/6J Mice

Nicotinamide adenine dinucleotide (NAD⁺) plays a vital role in cellular processes that govern human health and disease. Nicotinamide phosphoribosyltransferase (NAMPT) is a rate-limiting enzyme in NAD⁺ biosynthesis. Thus, boosting NAD⁺ level via an increase in NAMPT levels is an attractive approach for countering the effects of aging and metabolic disease. This study aimed to establish IRW (Ile-Arg-Trp), a small tripeptide derived from ovotransferrin, as a booster of NAMPT levels. Treatment of muscle (L6) cells with IRW increased intracellular NAMPT protein levels (2.2-fold, p < 0.05) and boosted NAD⁺ (p < 0.01). Both immunoprecipitation and recombinant NAMPT assays indicated the possible NAMPT-activating ability of IRW (p < 0.01). Similarly, IRW increased NAMPT mRNA and protein levels in the liver (2.6-fold, p < 0.01) and muscle tissues (2.3-fold, p < 0.05) of C57BL/6J mice fed with a high-fat diet (HFD). A significantly increased level of circulating NAD⁺ was also observed following IRW treatment (4.7 fold, p < 0.0001). Dosing of Drosophila melanogaster with IRW elevated both D-NAAM (fly NAMPT) and NAD⁺ in vivo (p < 0.05). However, IRW treatment did not boost NAMPT levels in SIRT1 KO cells, indicating a possible SIRT1 dependency for the pharmacological effect. Overall, these data indicate that IRW is a novel small peptide booster of the NAMPT pool.

Fast Rescoring Protocols to Improve the Performance of Structure-Based Virtual Screening Performed on Protein-Protein Interfaces

Protein-protein interactions (PPIs) are attractive targets for drug design because of their essential role in numerous cellular processes and disease pathways. However, in general, PPIs display exposed binding pockets at the interface, and as such, have been largely unexploited for therapeutic interventions with low-molecular weight compounds. Here, we used docking and various rescoring strategies in an attempt to recover PPI inhibitors from a set of active and inactive molecules for 11 targets collected in ChEMBL and PubChem. Our focus is on the screening power of the various developed protocols and on using fast approaches so as to be able to apply such a strategy to the screening of ultralarge libraries in the future. First, we docked compounds into each target using the fast "pscreen" mode of the structure-based virtual screening (VS) package Surflex. Subsequently, the docking poses were postprocessed to derive a set of 3D topological descriptors: (i) shape similarity and (ii) interaction fingerprint similarity with a co-crystallized inhibitor, (iii) solvent-accessible surface area, and (iv) extent of deviation from the geometric center of a reference inhibitor. The derivatized descriptors, together with descriptor-scaled scoring functions, were utilized to investigate possible impacts on VS performance metrics. Moreover, four standalone scoring functions, RF-Score-VS (machine-learning), DLIGAND2 (knowledge-based), Vinardo (empirical), and X-SCORE (empirical), were employed to rescore the PPI compounds. Collectively, the results indicate that the topological scoring algorithms could be valuable both at a global level, with up to 79% increase in areas under the receiver operating characteristic curve for some targets, and in early stages, with up to a 4-fold increase in enrichment factors at 1% of the screened collections. Outstandingly, DLIGAND2 emerged as the best scoring function on this data set, outperforming all rescoring techniques in terms of VS metrics. The described methodology could help in the rational design of small-molecule PPI inhibitors and has direct applications in many therapeutic areas, including cancer, CNS, and infectious diseases such as COVID-19.

Augmenting Hit Identification by Virtual Screening Techniques in Small Molecule Drug Discovery

Two orthogonal approaches for hit identification in drug discovery are large-scale in vitro and in silico screening. In recent years, due to the emergence of new targets and a rapid increase in the size of the readily synthesizable chemical space, there is a growing emphasis on the integration of the two techniques to improve the hit finding efficiency. Here, we highlight three examples of drug discovery projects at Merck & Co., Inc., Kenilworth, NJ, USA in which different virtual screening (VS) techniques, each specifically tailored to leverage knowledge available for the target, were utilized to augment the selection of high-quality chemical matter for in vitro assays and to enhance the diversity and tractability of hits. Central to success is a fully integrated workflow combining in silico and experimental expertise at every stage of the hit identification process. We advocate that workflows encompassing VS as part of an integrated hit finding plan should be widely adopted to accelerate hit identification and foster cross-functional collaborations in modern drug discovery.

Anticancer drug discovery by targeting cullin neddylation

Protein neddylation is a post-translational modification which transfers the ubiquitin-like protein NEDD8 to a lysine residue of the target substrate through a three-step enzymatic cascade. The best-known substrates of neddylation are cullin family proteins, which are the core component of Cullin–RING E3 ubiquitin ligases (CRLs). Given that cullin neddylation is required for CRL activity, and CRLs control the turn-over of a variety of key signal proteins and are often abnormally activated in cancers, targeting neddylation becomes a promising approach for discovery of novel anti-cancer therapeutics. In the past decade, we have witnessed significant progress in the field of protein neddylation from preclinical target validation, to drug screening, then to the clinical trials of neddylation inhibitors. In this review, we first briefly introduced the nature of protein neddylation and the regulation of neddylation cascade, followed by a summary of all reported chemical inhibitors of neddylation enzymes. We then discussed the structure-based targeting of protein–protein interaction in neddylation cascade, and finally the available approaches for the discovery of new neddylation inhibitors. This review will provide a focused, up-to-date and yet comprehensive overview on the discovery effort of neddylation inhibitors.

Cross-docking benchmark for automated pose and ranking prediction of ligand binding

Significant efforts have been devoted in the last decade to improving molecular docking techniques to predict both accurate binding poses and ranking affinities. Some shortcomings in the field are the limited number of standard methods for measuring docking success and the availability of widely accepted standard data sets for use as benchmarks in comparing different docking algorithms throughout the field. In order to address these issues, we have created a Cross-Docking Benchmark server. The server is a versatile cross-docking data set containing 4,399 protein-ligand complexes across 95 protein targets intended to serve as benchmark set and gold standard for state-of-the-art pose and ranking prediction in easy, medium, hard, or very hard docking targets. The benchmark along with a customizable cross-docking data set generation tool is available at http://disco.csb.pitt.edu. We further demonstrate the potential uses of the server in questions outside of basic benchmarking such as the selection of the ideal docking reference structure.

Cheminformatics Explorations of Natural Products

The chemistry of natural products is fascinating and has continuously attracted the attention of the scientific community for many reasons including, but not limited to, biosynthesis pathways, chemical diversity, the source of bioactive compounds and their marked impact on drug discovery. There is a broad range of experimental and computational techniques (molecular modeling and cheminformatics) that have evolved over the years and have assisted the investigation of natural products. Herein, we discuss cheminformatics strategies to explore the chemistry and applications of natural products. Since the potential synergisms between cheminformatics and natural products are vast, we will focus on three major aspects: (1) exploration of the chemical space of natural products to identify bioactive compounds, with emphasis on drug discovery; (2) assessment of the toxicity profile of natural products; and (3) diversity analysis of natural product collections and the design of chemical collections inspired by natural sources.

Rescoring and Linearly Combining: A Highly Effective Consensus Strategy for Virtual Screening Campaigns

The study proposes a novel consensus strategy based on linear combinations of different docking scores to be used in the evaluation of virtual screening campaigns. The consensus models are generated by applying the recently proposed Enrichment Factor Optimization (EFO) method, which develops the linear equations by exhaustively combining the available docking scores and by optimizing the resulting enrichment factors. The performances of such a consensus strategy were evaluated by simulating the entire Directory of Useful Decoys (DUD datasets). In detail, the poses were initially generated by the PLANTS docking program and then rescored by ReScore+ with and without the minimization of the complexes. The so calculated scores were then used to generate the mentioned consensus models including two or three different scoring functions. The reliability of the generated models was assessed by a per target validation as performed by default by the EFO approach. The encouraging performances of the here proposed consensus strategy are emphasized by the average increase of the 17% in the Top 1% enrichment factor (EF) values when comparing the single best score with the linear combination of three scores. Specifically, kinases offer a truly convincing demonstration of the efficacy of the here proposed consensus strategy since their Top 1% EF average ranges from 6.4 when using the single best performing primary score to 23.5 when linearly combining scoring functions. The beneficial effects of this consensus approach are clearly noticeable even when considering the entire DUD datasets as evidenced by the area under the curve (AUC) averages revealing a 14% increase when combining three scores. The reached AUC values compare very well with those reported in literature by an extended set of recent benchmarking studies and the three-variable models afford the highest AUC average.

Protein structure and computational drug discovery

The first protein structures revealed a complex web of weak interactions stabilising the three-dimensional shape of the molecule. Small molecule ligands were then found to exploit these same weak binding events to modulate protein function or act as substrates in enzymatic reactions. As the understanding of ligand–protein binding grew, it became possible to firstly predict how and where a particular small molecule might interact with a protein, and then to identify putative ligands for a specific protein site. Computer-aided drug discovery, based on the structure of target proteins, is now a well-established technique that has produced several marketed drugs. We present here an overview of the various methodologies being used for structure-based computer-aided drug discovery and comment on possible future developments in the field.