Reliability analysis and optimization of the consensus docking approach for the development of virtual screening studies

Watermelon: setup and validation of an in silico fragment-based approach

We present a new computational approach, named Watermelon, designed for the development of pharmacophore models based on receptor structures. The methodology involves the sampling of potential hotspots for ligand interactions within a protein target's binding site, utilising molecular fragments as probes. By employing docking and molecular dynamics (MD) simulations, the most significant interactions formed by these probes within distinct regions of the binding site are identified. These interactions are subsequently transformed into pharmacophore features that delineates key anchoring sites for potential ligands. The reliability of the approach was experimentally validated using the monoacylglycerol lipase (MAGL) enzyme. The generated pharmacophore model captured features representing ligand-MAGL interactions observed in various X-ray co-crystal structures and was employed to screen a database of commercially available compounds, in combination with consensus docking and MD simulations. The screening successfully identified two new MAGL inhibitors with micromolar potency, thus confirming the reliability of the Watermelon approach.

Molecular modelling and anticholinesterase activity of the essential oil from three chemotypes of Lippia alba (Mill.) N.E.Br. ex Britton & P. Wilson (Verbenaceae)

Lippia alba (Mill.) N.E. Brown (Verbenaceae), popularly known as "erva cidreira", is one of the most used plants in Brazilian folk medicine. The species has several chemotypes and its volatile constituents have already been characterized, and present different chemical markers with known pharmacological properties, such as analgesic, sedative and antifungal properties. The objective of this study was to evaluate the anticholinesterase activity (AChE) of the essential oil of three chemotypes of Lippia alba and, by using molecular anchoring, determine the best receptor-ligand interaction energies of the main constituents present in the samples of oil. The essential oils were obtained via hydrodistillation (LA1 and LA2) and steam drag (LA3), and their volatile constituents determined using GC-MS. For the determination of anticholinesterase activity, direct bioautography and colorimetry assays based on Ellman's method were used. Molecular docking was performed using a multiple solution genetic algorithm and Merck molecular force field 94 (MMFF94) as the scoring function. In the main constituents of the oil samples, three chemotypes were identified for L. alba: LA1 is rich in citral, LA2 is rich in carvone and LA3 is rich in linalool. All L. alba chemotypes showed AChE enzyme inhibition with an IC50 of 3.57 μg/mL (LA1), 0.1 μg/mL (LA2) and 4.34 μg/mL (LA3). The molecular docking study complemented the results of the experiment and demonstrated significant interactions between the main constituents of the oils and the amino acid residues of the AChE enzyme. Irrespective of the chemotype, Lippia alba presents biotechnological potential for the discovery of anticholinesterase substances, with the chemotype LA2 (rich in carvone) being the most active.

SMS121, a new inhibitor of CD36, impairs fatty acid uptake and viability of acute myeloid leukemia

Acute myeloid leukemia (AML) is the most common form of acute leukemia in adults and the second most common among children. AML is characterized by aberrant proliferation of myeloid blasts in the bone marrow and impaired normal hematopoiesis. Despite the introduction of new drugs and allogeneic bone marrow transplantation, patients have poor overall survival rate with relapse as the major challenge, driving the demand for new therapeutic strategies. AML patients with high expression of the very long/long chain fatty acid transporter CD36 have poorer survival and very long chain fatty acid metabolism is critical for AML cell survival. Here we show that fatty acids are transferred from human primary adipocytes to AML cells upon co-culturing. A drug-like small molecule (SMS121) was identified by receptor-based virtual screening and experimentally demonstrated to target the lipid uptake protein CD36. SMS121 reduced the uptake of fatty acid into AML cells that could be reversed by addition of free fatty acids and caused decreased cell viability. The data presented here serves as a framework for the development of CD36 inhibitors to be used as future therapeutics against AML.

Discovery of novel IDO1/TDO2 dual inhibitors: a consensus Virtual screening approach with molecular dynamics simulations, and binding free energy analysis

The pursuit of effective cancer immunotherapy drugs remains challenging, with overexpression of indoleamine 2,3-dioxygenase 1 (IDO1) and tryptophan 2,3-dioxygenase 2 (TDO2) allowing cancer cells to evade immune attacks. While several IDO1 inhibitors have undergone clinical testing, only three dual IDO1/TDO2 inhibitors have reached human trials. Hence, this study focuses on identifying novel IDO1/TDO2 dual inhibitors through consensus structure-based virtual screening (SBVS). ZINC15 natural products library was refined based on molecular descriptors, and the selected compounds were docked to the holo form IDO1 and TDO2 using two different software programs and ranked according to their consensus docking scores. The top-scoring compounds underwent in silico evaluations for pharmacokinetics, toxicity, CYP3A4 affinity, molecular dynamics (MD) simulations, and MM-GBSA binding free energy calculations. Five compounds (ZINC00000079405/10, ZINC00004028612/11, ZINC00013380497/12, ZINC00014613023/13, and ZINC00103579819/14) were identified as potential IDO1/TDO2 dual inhibitors due to their high consensus docking scores, key residue interactions with the enzymes, favorable pharmacokinetics, and avoidance of CYP3A4 binding. MD simulations of the top three hits with IDO1 indicated conformational changes and compactness, while MM-GBSA analysis revealed strong binding free energy for compounds 10 (ΔG: -20.13 kcal/mol) and 11 (ΔG: -16.22 kcal/mol). These virtual hits signify a promising initial step in identifying candidates as supplementary therapeutics to immune checkpoint inhibitors in cancer treatment. Their potential to deliver potent dual inhibition of IDO1/TDO2, along with safety and favorable pharmacokinetics, makes them compelling. Validation through in vitro and in vivo assays should be conducted to confirm their activity, selectivity, and preclinical potential as holo IDO1/TDO2 dual inhibitors.Communicated by Ramaswamy H. Sarma.

Computational-based investigation of antioxidative potential polyphenolic compounds of Salvia officinalis L.: combined DFT and molecular docking approaches

CONTEXT

The antioxidant properties of the three polyphenolic compounds (carnosol, cirsiliol, and luteolin) of Salvia officinalis L. were investigated employing the density functional theory (DFT) calculations at the B3LYP of basis set at 6-311 +  + G (d, p) in order to evaluate their antioxidant activity. The enthalpies of reactions associated with the SET-PT, SPLET, and HAT mechanisms were analyzed in gas and in different solvents using the CPCM (conductor-like polarizable continuum) model. For all possible hydrogen donor sites, the corresponding parameters (BDE, AIP, PDE, PA, ETE, HOMOs, and LUMOs) and reactivity indices (IPE, EA, Χ, η, S, and ω) were also evaluated. The calculated results showed that derivatives 12-OH, 11-OH, 4'-OH, and 3'-OH had the lowest antioxidant activity. The results showed as well that carnosol, cirsiliol, and luteolin have higher reactivity compared to ascorbic acid and could be considered better antioxidants. According to research, the catechol group is crucial in influencing the studied compounds antioxidant activity. The theoretically predicted order of antioxidant efficiencies in this work agrees well with the QSAR (quantitative structure-activity relationship) data. The findings show that in the vacuum as well as benzene media. HAT would be the most effective mechanism; in contrast, the thermodynamic equilibrium approach in polar media is the SPLET mechanism. Likewise, the outcomes of the docking modeling confirm that the selected molecules have high inhibitory activity to glutathione-S-transferases (GSTs) receptors. Moreover, they have very important pharmacokinetic, chemical, and biological profiles. Finally, all the results show that the three natural molecules have good pharmacokinetic profiles, particularly the bioavailability and permeability toward biological membranes.

METHODS

The software packages used in this investigation are Gaussian 16, Discovery studio Visualizer, and AutoDock vina. The three compounds (carnosol, cirsiliol, and luteolin) of Salvia officinalis L. were optimized with DFT/B3LYP of basis set at 6-311 +  + G (d, p). The optimized structures were established via vibrational analysis (i.e., no imaginary frequencies in the frequency set). All enthalpies were zero-point (ZPE) corrected. Vibrational frequency calculations were performed at 298.15 K and 1 atmosphere pressure to determine the thermodynamic characteristics of the investigated reactions. The descriptors were associated with the antioxidant mechanisms for investigated molecules in vacuum and in various solvents. The molecular docking was used by AutoDock vina to estimate and evaluate the title compounds compatibility as potential antioxidant drugs utilizing appropriate receptor proteins. The solvation effect in the medium of benzene (ɛ = 2.27) and water (ɛ = 78.39) was taken into account. Furthermore, a methanol solvent (ɛ = 32.61) was also taken into consideration to compare with the empirical data.

DockOpt: A Tool for Automatic Optimization of Docking Models

Molecular docking is a widely used technique for leveraging protein structure for ligand discovery, but it remains difficult to utilize due to limitations that have not been adequately addressed. Despite some progress toward automation, docking still requires expert guidance, hindering its adoption by a broader range of investigators. To make docking more accessible, we developed a new utility called DockOpt, which automates the creation, evaluation, and optimization of docking models prior to their deployment in large-scale prospective screens. DockOpt outperforms our previous automated pipeline across all 43 targets in the DUDE-Z benchmark data set, and the generated models for 84% of targets demonstrate sufficient enrichment to warrant their use in prospective screens, with normalized LogAUC values of at least 15%. DockOpt is available as part of the Python package Pydock3 included in the UCSF DOCK 3.8 distribution, which is available for free to academic researchers at https://dock.compbio.ucsf.edu and free for everyone upon registration at https://tldr.docking.org.

Design, synthesis, anti-acetylcholinesterase evaluation and molecular modelling studies of novel coumarin-chalcone hybrids

The major enzyme responsible for the hydrolytic breakdown of the neurotransmitter acetylcholine (ACh) is acetylcholinesterase (AChE). Acetylcholinesterase inhibitors (AChEIs) are the most prescribed class of medications for the treatment of Alzheimer's disease (AD) and dementia. The limitations of available therapy, like side effects, drug tolerance, and inefficacy in halting disease progression, drive the need for better, more efficacious, and safer drugs. In this study, a series of fourteen novel chalcone-coumarin derivatives (8a-n) were designed, synthesized and characterized by spectral techniques like FT-IR, NMR, and HR-MS. Subsequently, the synthesized compounds were tested for their ability to inhibit acetylcholinesterase (AChE) activity by Ellman's method. All tested compounds showed AChE inhibition with IC50 value ranging from 0.201 ± 0.008 to 1.047 ± 0.043 μM. Hybrid 8d having chloro substitution on ring-B of the chalcone scaffold showed relatively better potency, with IC50 value of 0.201 ± 0.008 μM compared to other members of the series. The reference drug, galantamine, exhibited an IC50 at 1.142 ± 0.027 μM. Computational studies revealed that designed compounds bind to the peripheral anionic site (PAS), the catalytic active site (CAS), and the mid-gorge site of AChE. Putative binding modes, ligand-enzyme interactions, and stability of the best active compound are studied using molecular docking, followed by molecular dynamics (MD) simulations. The cytotoxicity of the synthesised derivatives was determined using the MTT test at three concentrations (100 g/mL, 500 g/mL, and 1 mg/mL). None of the chemicals had a significant effect on the body at the highest dose of 1 mg/mL.Communicated by Ramaswamy H. Sarma.

Identification of Human Dihydroorotate Dehydrogenase Inhibitor by a Pharmacophore-Based Virtual Screening Study

Human dihydroorotate dehydrogenase (hDHODH) is an enzyme belonging to a flavin mononucleotide (FMN)-dependent family involved in de novo pyrimidine biosynthesis, a key biological pathway for highly proliferating cancer cells and pathogens. In fact, hDHODH proved to be a promising therapeutic target for the treatment of acute myelogenous leukemia, multiple myeloma, and viral and bacterial infections; therefore, the identification of novel hDHODH ligands represents a hot topic in medicinal chemistry. In this work, we reported a virtual screening study for the identification of new promising hDHODH inhibitors. A pharmacophore-based approach combined with a consensus docking analysis and molecular dynamics simulations was applied to screen a large database of commercial compounds. The whole virtual screening protocol allowed for the identification of a novel compound that is endowed with promising inhibitory activity against hDHODH and is structurally different from known ligands. These results validated the reliability of the in silico workflow and provided a valuable starting point for hit-to-lead and future lead optimization studies aimed at the development of new potent hDHODH inhibitors.

VenomPred: A Machine Learning Based Platform for Molecular Toxicity Predictions

The use of in silico toxicity prediction methods plays an important role in the selection of lead compounds and in ADMET studies since in vitro and in vivo methods are often limited by ethics, time, budget and other resources. In this context, we present our new web tool VenomPred, a user-friendly platform for evaluating the potential mutagenic, hepatotoxic, carcinogenic and estrogenic effects of small molecules. VenomPred platform employs several in-house Machine Learning (ML) models developed with datasets derived from VEGA QSAR, a software that includes a comprehensive collection of different toxicity models and has been used as a reference for building and evaluating our ML models. The results showed that our models achieved equal or better performance than those obtained with the reference models included in VEGA QSAR. In order to improve the predictive performance of our platform, we adopted a consensus approach combining the results of different ML models, which was able to predict chemical toxicity better than the single models. This improved method was thus implemented in the VenomPred platform, a freely accessible webserver that takes the SMILES (Simplified Molecular-Input Line-Entry System) strings of the compounds as input and sends the prediction results providing a probability score about their potential toxicity.

An Electric Field–Based Approach for Quantifying Effective Volumes and Radii of Chemically Affected Space

Chemical shape and size play a critical role in chemistry. The van der Waals (vdW) radius, a familiar manifold used to quantify size by assuming overlapping spheres, provides rapid estimates of size in atoms, molecules, and materials. However, the vdW method may be too rigid to describe highly polarized systems and chemical species that stray from spherical atomistic environments. To deal with these exotic chemistries, numerous alternate methods based on electron density have been presented. While each boasts inherent generality, all define the size of a chemical system, in one way or another, by its electron density. Herein, we revisit the longstanding problem of assessing sizes of atoms and molecules, instead through examination of the local electric field produced by them. While conceptually different than nuclei-centered methods like that of van der Waals, the field assesses chemically affected volumes. This approach implicitly accounts for long-range fields in highly polar systems and predicts that cations should affect more space than neutral counterparts.

Computing atomic and molecular volumes from DFT and ab initio-based electric fields.

New PIN1 inhibitors identified through a pharmacophore-driven, hierarchical consensus docking strategy

PIN1 is considered as a therapeutic target for a wide variety of tumours. However, most of known inhibitors are devoid of cellular activity despite their good enzyme inhibitory profile. Hence, the lack of effective compounds for the clinic makes the identification of novel PIN1 inhibitors a hot topic in the medicinal chemistry field. In this work, we reported a virtual screening study for the identification of new promising PIN1 inhibitors. A receptor-based procedure was applied to screen different chemical databases of commercial compounds. Based on the whole workflow, two compounds were selected and biologically evaluated. Both ligands, compounds VS1 and VS2, showed a good enzyme inhibitory activity and VS2 also demonstrated a promising antitumoral activity in ovarian cancer cells. These results confirmed the reliability of our in silico protocol and provided a structurally novel ligand as a valuable starting point for the development of new PIN1 inhibitors.

Combination of pose and rank consensus in docking-based virtual screening: the best of both worlds

The use of high-throughput docking (HTD) in the drug discovery pipeline is today widely established. In spite of methodological improvements in docking accuracy (pose prediction), scoring power, ranking power, and screening power in HTD remain challenging. In fact, pose prediction is of critical importance in view of the pose-dependent scoring process, since incorrect poses will necessarily decrease the ranking power of scoring functions. The combination of results from different docking programs (consensus scoring) has been shown to improve the performance of HTD. Moreover, it has been also shown that a pose consensus approach might also result in database enrichment. We present a new methodology named Pose/Ranking Consensus (PRC) that combines both pose and ranking consensus approaches, to overcome the limitations of each stand-alone strategy. This approach has been developed using four docking programs (ICM, rDock, Auto Dock 4, and PLANTS; the first one is commercial, the other three are free). We undertook a thorough analysis for the best way of combining pose and rank strategies, and applied the PRC to a wide range of 34 targets sampling different protein families and binding site properties. Our approach exhibits an improved systematic performance in terms of enrichment factor and hit rate with respect to either pose consensus or consensus ranking alone strategies at a lower computational cost, while always ensuring the recovery of a suitable number of ligands. An analysis using four free docking programs (replacing ICM by Auto Dock Vina) displayed comparable results.

The new methodology named Pose/Ranking Consensus (PRC) combines both pose and ranking consensus strategies. It displays an enhanced performance in terms of enrichment factor and hit rate, ensuring the recovery of a suitable number of ligands.

STARD3: A Prospective Target for Cancer Therapy

Cancer is one of the major causes of death in developed countries and current therapies are based on surgery, chemotherapeutic agents, and radiation. To overcome side effects induced by chemo- and radiotherapy, in recent decades, targeted therapies have been proposed in second and even first lines. Targeted drugs act on the essential pathways involved in tumor induction, progression, and metastasis, basically all the hallmark of cancers. Among emerging pathways, the cholesterol metabolic pathway is a strong candidate for this purpose. Cancer cells have an accelerated metabolic rate and require a continuous supply of cholesterol for cell division and membrane renewal. Steroidogenic acute regulatory related lipid transfer (START) proteins are a family of proteins involved in the transfer of lipids and some of them are important in non-vesicular cholesterol transportation within the cell. The alteration of their expression levels is implicated in several diseases, including cancers. In this review, we report the latest discoveries on StAR-related lipid transfer protein domain 3 (STARD3), a member of the START family, which has a potential role in cancer, focusing on the structural and biochemical characteristics and mechanisms that regulate its activity. The role of the STARD3 protein as a molecular target for the development of cancer therapies is also discussed. As STARD3 is a key protein in the cholesterol movement in cancer cells, it is of interest to identify inhibitors able to block its activity.

Recent Advances in In Silico Target Fishing

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.

Novel Bis-Thiazole Derivatives: Synthesis and Potential Cytotoxic Activity Through Apoptosis With Molecular Docking Approaches

A series of bis-thiazoles 5a-g were synthesized from bis-thiosemicarbazone 3 with hydrazonoyl chlorides 4a-g. Reaction of 3 with two equivalents of α-halocarbonyl compounds 6-8, 10, and 12a-d afforded the corresponding bis-thiazolidines 9, 11, and 13a-d, respectively. Condensation of bis-thiazolidin-4-one 9 with different aromatic aldehydes furnished bis-thiazolidin-4-ones 14a-d. Compounds 5a-g, 9, and 13a,c,d were screened in vitro for their cytotoxic activities in a panel of cancer cell lines. Compounds 5a-c, 5f-g, and 9 exhibited remarkable cytotoxic activities, especially compound 5c with potent IC50 value 0.6 nM (against cervical cancer, Hela cell line) and compound 5f with high IC50 value 6 nM (against ovarian cancer, KF-28 cell line). Compound 5f-induced appreciated apoptotic cell death was measured as 82.76% associated with cell cycle arrest at the G1 phase. The apoptotic pathways activated in KF-28 cells treated with 5a, 5b, and 5f were further investigated. The upregulation of some pro-apoptotic genes, bax and puma, and the downregulation of some anti-apoptotic genes including the Bcl-2 gene were observed, indicating activation of the mitochondrial-dependent apoptosis. Together with the molecular docking studies of compounds 5a and 5b, our data revealed potential Pim-1 kinase inhibition through their high binding affinities indicated by inhibition of phosphorylated C-myc as a downstream target for Pim-1 kinase. Our study introduces a set of bis-thiazoles with potent anti-cancer activities, in vitro.

VirtualFlow Ants-Ultra-Large Virtual Screenings with Artificial Intelligence Driven Docking Algorithm Based on Ant Colony Optimization

The docking program PLANTS, which is based on ant colony optimization (ACO) algorithm, has many advanced features for molecular docking. Among them are multiple scoring functions, the possibility to model explicit displaceable water molecules, and the inclusion of experimental constraints. Here, we add support of PLANTS to VirtualFlow (VirtualFlow Ants), which adds a valuable method for primary virtual screenings and rescoring procedures. Furthermore, we have added support of ligand libraries in the MOL2 format, as well as on the fly conversion of ligand libraries which are in the PDBQT format to the MOL2 format to endow VirtualFlow Ants with an increased flexibility regarding the ligand libraries. The on the fly conversion is carried out with Open Babel and the program SPORES. We applied VirtualFlow Ants to a test system involving KEAP1 on the Google Cloud up to 128,000 CPUs, and the observed scaling behavior is approximately linear. Furthermore, we have adjusted several central docking parameters of PLANTS (such as the speed parameter or the number of ants) and screened 10 million compounds for each of the 10 resulting docking scenarios. We analyzed their docking scores and average docking times, which are key factors in virtual screenings. The possibility of carrying out ultra-large virtual screening with PLANTS via VirtualFlow Ants opens new avenues in computational drug discovery.

Discovery of Monoacylglycerol Lipase (MAGL) Inhibitors Based on a Pharmacophore-Guided Virtual Screening Study

Monoacylglycerol lipase (MAGL) is an important enzyme of the endocannabinoid system that catalyzes the degradation of the major endocannabinoid 2-arachidonoylglycerol (2-AG). MAGL is associated with pathological conditions such as pain, inflammation and neurodegenerative diseases like Parkinson’s and Alzheimer’s disease. Furthermore, elevated levels of MAGL have been found in aggressive breast, ovarian and melanoma cancer cells. Due to its different potential therapeutic implications, MAGL is considered as a promising target for drug design and the discovery of novel small-molecule MAGL inhibitors is of great interest in the medicinal chemistry field. In this context, we developed a pharmacophore-based virtual screening protocol combined with molecular docking and molecular dynamics simulations, which showed a final hit rate of 50% validating the reliability of the in silico workflow and led to the identification of two promising and structurally different reversible MAGL inhibitors, VS1 and VS2. These ligands represent a valuable starting point for structure-based hit-optimization studies aimed at identifying new potent MAGL inhibitors.

Consensus Docking in Drug Discovery

Background:

Molecular docking is probably the most popular and profitable approach in computer-aided drug design, being the staple technique for predicting the binding mode of bioactive compounds and for performing receptor-based virtual screening studies. The growing attention received by docking, as well as the need for improving its reliability in pose prediction and virtual screening performance, has led to the development of a wide plethora of new docking algorithms and scoring functions. Nevertheless, it is unlikely to identify a single procedure outperforming the other ones in terms of reliability and accuracy or demonstrating to be generally suitable for all kinds of protein targets.

Methods:

In this context, consensus docking approaches are taking hold in computer-aided drug design. These computational protocols consist in docking ligands using multiple docking methods and then comparing the binding poses predicted for the same ligand by the different methods. This analysis is usually carried out calculating the root-mean-square deviation among the different docking results obtained for each ligand, in order to identify the number of docking methods producing the same binding pose.

Results:

The consensus docking approaches demonstrated to improve the quality of docking and virtual screening results compared to the single docking methods. From a qualitative point of view, the improvement in pose prediction accuracy was obtained by prioritizing ligand binding poses produced by a high number of docking methods, whereas with regards to virtual screening studies, high hit rates were obtained by prioritizing the compounds showing a high level of pose consensus.

Conclusion:

In this review, we provide an overview of the results obtained from the performance assessment of various consensus docking protocols and we illustrate successful case studies where consensus docking has been applied in virtual screening studies.

Discovery of a new ATP-citrate lyase (ACLY) inhibitor identified by a pharmacophore-based virtual screening study

ATP citrate lyase (ACLY) is an important enzyme that catalyzes the conversion of citrate to acetyl-CoA in normal cells, facilitating the de novo fatty acid synthesis. Lipids and fatty acids were found to be accumulated in different types of tumors, such as brain, breast, rectal and ovarian cancer, representing a great source of energy for cancer cell growth and metabolism. Since ACLY-mediated conversion of citrate to acetyl-CoA constitutes the basis for fatty acid synthesis, ACLY seems to be quite an unexplored and promising therapeutic target for anticancer drug design. A pharmacophore-based virtual screening (VS) protocol with the aid of hierarchical docking, consensus docking (CD), molecular dynamics (MD) simulations and ligand-protein binding free energy calculations led to the identification of compound VS1, which showed a moderate but promising inhibitory activity, demonstrating to be 2.5 times more potent than reference inhibitor 2-hydroxycitrate. These results validate the reliability of our VS workflow and pave the way for the design of novel and more potent ACLY inhibitors.Communicated by Ramaswamy H. Sarma.

Structure-Based Virtual Screening: From Classical to Artificial Intelligence

The drug development process is a major challenge in the pharmaceutical industry since it takes a substantial amount of time and money to move through all the phases of developing of a new drug. One extensively used method to minimize the cost and time for the drug development process is computer-aided drug design (CADD). CADD allows better focusing on experiments, which can reduce the time and cost involved in researching new drugs. In this context, structure-based virtual screening (SBVS) is robust and useful and is one of the most promising in silico techniques for drug design. SBVS attempts to predict the best interaction mode between two molecules to form a stable complex, and it uses scoring functions to estimate the force of non-covalent interactions between a ligand and molecular target. Thus, scoring functions are the main reason for the success or failure of SBVS software. Many software programs are used to perform SBVS, and since they use different algorithms, it is possible to obtain different results from different software using the same input. In the last decade, a new technique of SBVS called consensus virtual screening (CVS) has been used in some studies to increase the accuracy of SBVS and to reduce the false positives obtained in these experiments. An indispensable condition to be able to utilize SBVS is the availability of a 3D structure of the target protein. Some virtual databases, such as the Protein Data Bank, have been created to store the 3D structures of molecules. However, sometimes it is not possible to experimentally obtain the 3D structure. In this situation, the homology modeling methodology allows the prediction of the 3D structure of a protein from its amino acid sequence. This review presents an overview of the challenges involved in the use of CADD to perform SBVS, the areas where CADD tools support SBVS, a comparison between the most commonly used tools, and the techniques currently used in an attempt to reduce the time and cost in the drug development process. Finally, the final considerations demonstrate the importance of using SBVS in the drug development process.

Molecular modeling and LC-MS-based metabolomics of a glutamine-valproic acid (Gln-VPA) derivative on HeLa cells

Glutaminase plays an important role in carcinogenesis and cancer cell growth. This biological target is interesting against cancer cells. Therefore, in this work, in silico [docking and molecular dynamics (MD) simulations] and in vitro methods (antiproliferative and LC-MS metabolomics) were employed to assay a hybrid compound derived from glutamine and valproic acid (Gln-VPA), which was compared with 6-diazo-5-oxo-L-norleucine (DON, a glutaminase inhibitor) and VPA (contained in Gln-VPA structure). Docking results from some snapshots retrieved from MD simulations show that glutaminase recognized Gln-VPA and DON. Additionally, Gln-VPA showed antiproliferative effects in HeLa cells and inhibited glutaminase activity. Finally, the LC-MS-based metabolomics studies on HeLa cells treated with either Gln-VPA (IC₆₀ = 8 mM) or DON (IC₅₀ = 3.5 mM) show different metabolomics behaviors, suggesting that they modulate different biological targets of the cell death mechanism. In conclusion, Gln-VPA is capable of interfering with more than one pharmacological target of cancer, making it an interesting drug that can be used to avoid multitherapy of classic anticancer drugs.

Application of MM-PBSA Methods in Virtual Screening

Computer-aided drug design techniques are today largely applied in medicinal chemistry. In particular, receptor-based virtual screening (VS) studies, in which molecular docking represents the gold standard in silico approach, constitute a powerful strategy for identifying novel hit compounds active against the desired target receptor. Nevertheless, the need for improving the ability of docking in discriminating true active ligands from inactive compounds, thus boosting VS hit rates, is still pressing. In this context, the use of binding free energy evaluation approaches can represent a profitable tool for rescoring ligand-protein complexes predicted by docking based on more reliable estimations of ligand-protein binding affinities than those obtained with simple scoring functions. In the present review, we focused our attention on the Molecular Mechanics-Poisson Boltzman Surface Area (MM-PBSA) method for the calculation of binding free energies and its application in VS studies. We provided examples of successful applications of this method in VS campaigns and evaluation studies in which the reliability of this approach has been assessed, thus providing useful guidelines for employing this approach in VS.

Molecular Architect: A User-Friendly Workflow for Virtual Screening

Computer-assisted drug design (CADD) methods have greatly contributed to the development of new drugs. Among CADD methodologies, virtual screening (VS) can enrich the compound collection with molecules that have the desired physicochemical and pharmacophoric characteristics that are needed to become drugs. Many free tools are available for this purpose, but they are difficult to use and do not have a graphical user interface. Furthermore, several free tools must be used to carry out the entire VS process, requiring the user to process the results of one software program so that they can be used in another program, adding a potential source of human error. Moreover, some software programs require knowledge of advanced computational skills, such as programming languages. This context has motivated us to develop Molecular Architect (MolAr). MolAr is a workflow with a simple and intuitive interface that acts in an integrated and automated form to perform the entire VS process, from protein preparation (homology modeling and protonation state) to virtual screening. MolAr carries out VS through AutoDock Vina, DOCK 6, or a consensus of the two. Two case studies were conducted to demonstrate the performance of MolAr. In the first study, the feasibility of using MolAr for DNA-ligand systems was assessed. Both AutoDock Vina and DOCK 6 showed good results in performing VS in DNA-ligand systems. However, the use of consensus virtual screening was able to enrich the results. According to the area under the ROC curve and the enrichment factors, consensus VS was better able to predict the positions of the active ligands. The second case study was performed on 8 targets from the DUD-E database and 10 active ligands for each target. The results demonstrated that using the final ligand conformation provided by AutoDock Vina as an input for DOCK 6 improved the DOCK 6 ROC curves by up to 42% in VS. These case studies demonstrated that MolAr is capable conducting the VS process and is an easy-to-use and effective tool. MolAr is available for download free of charge at http: //www.drugdiscovery.com.br/software/.

Development of a cheminformatics platform for selectivity analyses of carbonic anhydrase inhibitors

The selectivity for a specific human Carbonic Anhydrase (hCA) isoform is an important property a hCA inhibitor (CAI) should be endowed with, in order to constitute a valuable therapeutic tool for the treatment of a desired pathology. In this context, we developed a chemoinformatic platform that allows the analysis of the structure and selectivity profile of known CAIs reported in literature, with the aim of identifying structural motifs connected to ligand selectivity, thus providing useful guidelines for the design of novel ligands selective for the desired hCA isoform. The platform is able to perform ultrafast structure and selectivity analyses through ligand fingerprint similarity, with no need of structural information about the target receptor and ligands' binding mode. It is easily accessible to the non-expert user through the implementation of a KNIME Analytic Platform workflow and could be extended to analyze the selectivity profile of known ligands of different target proteins.

Consensus Analyses in Molecular Docking Studies Applied to Medicinal Chemistry

The increasing number of computational studies in medicinal chemistry involving molecular docking has put the technique forward as promising in Computer-Aided Drug Design. Considering the main method in the virtual screening based on the structure, consensus analysis of docking has been applied in several studies to overcome limitations of algorithms of different programs and mainly to increase the reliability of the results and reduce the number of false positives. However, some consensus scoring strategies are difficult to apply and, in some cases, are not reliable due to the small number of datasets tested. Thus, for such a methodology to be successful, it is necessary to understand why, when and how to use consensus docking. Therefore, the present study aims to present different approaches to docking consensus, applications, and several scoring strategies that have been successful and can be applied in future studies.

Research Techniques Made Simple: Molecular Docking in Dermatology - A Foray into In Silico Drug Discovery

Drug discovery is a complex process with many potential pitfalls. To go to market, a drug must undergo extensive preclinical optimization followed by clinical trials to establish its efficacy and minimize toxicity and adverse events. The process can take 10-15 years and command vast research and development resources costing over $1 billion. The success rates for new drug approvals in the United States are < 15%, and investment costs often cannot be recouped. With the increasing availability of large public datasets (big data) and computational capabilities, data science is quickly becoming a key component of the drug discovery pipeline. One such computational method, large-scale molecular modeling, is critical in the preclinical hit and lead identification process. Molecular modeling involves the study of the chemical structure of a drug and how it interacts with a potential disease-relevant target, as well as predicting its ADMET properties. The scope of molecular modeling is wide and complex. Here we specifically discuss docking, a tool commonly employed for studying drug-target interactions. Docking allows for the systematic exploration of how a drug interacts at a protein binding site and allows for the rank-ordering of drug libraries for prioritization in subsequent studies. This process can be efficiently used to virtually screen libraries containing over millions of compounds.

Discovery of Novel µ-Opioid Receptor Inverse Agonist from a Combinatorial Library of Tetrapeptides through Structure-Based Virtual Screening

Morphine, oxycodone, fentanyl, and other µ-opioid receptors (MOR) agonists have been used for decades in antinociceptive therapies. However, these drugs are associated with numerous side effects, such as euphoria, addiction, respiratory depression, and adverse gastrointestinal reactions, thus, circumventing these drawbacks is of extensive importance. With the aim of identifying novel peptide ligands endowed with MOR inhibitory activity, we developed a virtual screening protocol, including receptor-based pharmacophore screening, docking studies, and molecular dynamics simulations, which was used to filter an in-house built virtual library of tetrapeptide ligands. The three top-scored compounds were synthesized and subjected to biological evaluation, revealing the identity of a hit compound (peptide 1) endowed with appreciable MOR inverse agonist effect and selectivity over δ-opioid receptors. These results confirmed the reliability of our computational approach and provided a promising starting point for the development of new potent MOR modulators.

Key Topics in Molecular Docking for Drug Design

Molecular docking has been widely employed as a fast and inexpensive technique in the past decades, both in academic and industrial settings. Although this discipline has now had enough time to consolidate, many aspects remain challenging and there is still not a straightforward and accurate route to readily pinpoint true ligands among a set of molecules, nor to identify with precision the correct ligand conformation within the binding pocket of a given target molecule. Nevertheless, new approaches continue to be developed and the volume of published works grows at a rapid pace. In this review, we present an overview of the method and attempt to summarise recent developments regarding four main aspects of molecular docking approaches: (i) the available benchmarking sets, highlighting their advantages and caveats, (ii) the advances in consensus methods, (iii) recent algorithms and applications using fragment-based approaches, and (iv) the use of machine learning algorithms in molecular docking. These recent developments incrementally contribute to an increase in accuracy and are expected, given time, and together with advances in computing power and hardware capability, to eventually accomplish the full potential of this area.

Virtual screening identifies a PIN1 inhibitor with possible antiovarian cancer effects

Peptidyl-prolyl cis-trans isomerase, NIMA-interacting 1 (PIN1) is a peptidyl-prolyl isomerase that binds phospho-Ser/Thr-Pro motifs in proteins and catalyzes the cis-trans isomerization of proline peptide bonds. PIN1 is overexpressed in several cancers including high-grade serous ovarian cancer. Since few therapies are effective against this cancer, PIN1 could be a therapeutic target but effective PIN1 inhibitors are lacking. To identify molecules with in vivo inhibitory effects on PIN1, we used consensus docking to model existing PIN1-ligand X-ray structures and to screen a chemical database for candidate inhibitors. Ten molecules were selected and tested in cellular assays, leading to the identification of VS10 that bound and inhibited PIN1. VS10 treatment reduced the viability of ovarian cancer cell lines by inducing proteasomal PIN1 degradation, without effects on PIN1 transcription, and also reduced the levels of downstream targets β-catenin, cyclin D1, and pSer473-Akt. VS10 is a selective PIN1 inhibitor that may offer new opportunities for treating PIN1-overexpressing tumors.

Combined Minimum-Run Resolution IV and Central Composite Design for Optimized Removal of the Tetracycline Drug Over Metal⁻Organic Framework-Templated Porous Carbon

In this study, a minimum-run resolution IV and central composite design have been developed to optimize tetracycline removal efficiency over mesoporous carbon derived from the metal-organic framework MIL-53 (Fe) as a self-sacrificial template. Firstly, minimum-run resolution IV, powered by the Design-Expert program, was used as an efficient and reliable screening study for investigating a set of seven factors, these were: tetracycline concentration (A: 5-15 mg/g), dose of mesoporous carbons (MPC) (B: 0.05-0.15 g/L), initial pH level (C: 2-10), contact time (D: 1-3 h), temperature (E: 20-40 °C), shaking speed (F: 150-250 rpm), and Na+ ionic strength (G: 10-90 mM) at both low (-1) and high (+1) levels, for investigation of the data ranges. The 20-trial model was analyzed and assessed by Analysis of Variance (ANOVA) data, and diagnostic plots (e.g., the Pareto chart, and half-normal and normal probability plots). Based on minimum-run resolution IV, three factors, including tetracycline concentration (A), dose of MPC (B), and initial pH (C), were selected to carry out the optimization study using a central composite design. The proposed quadratic model was found to be statistically significant at the 95% confidence level due to a low P-value (<0.05), high R2 (0.9078), and the AP ratio (11.4), along with an abundance of diagnostic plots (3D response surfaces, Cook's distance, Box-Cox, DFFITS, Leverage versus run, residuals versus runs, and actual versus predicted). Under response surface methodology-optimized conditions (e.g., tetracycline concentration of 1.9 mg/g, MPC dose of 0.15 g/L, and pH level of 3.9), the highest tetracycline removal efficiency via confirmation tests reached up to 98.0%-99.7%. Also, kinetic intraparticle diffusion and isotherm models were systematically studied to interpret how tetracycline molecules were absorbed on an MPC structure. In particular, the adsorption mechanisms including "electrostatic attraction" and "π-π interaction" were proposed.

First-of-its-kind STARD3 Inhibitor: In Silico Identification and Biological Evaluation as Anticancer Agent

STARD3 is a cellular protein that represents an attractive target for cancer therapy, being overexpressed in breast cancer and implied in the development of colorectal, gastric, and prostate cancers. Unfortunately, no STARD3 inhibitor has been identified yet. In this work, an in silico strategy was applied to predict a reliable binding mode of cholesterol into STARD3 and to develop a pharmacophore-based virtual screening protocol that allowed the identification of the first STARD3 inhibitor ever reported. The identified compound VS1 binds STARD3 with micromolar affinity (IC₅₀ = 35 μM) and shows antiproliferative activity in breast (MCF7 and MDA- MB-231) and colon (HCT-116) cancer cell lines in the same concentration range (IC₅₀ = 49.7-105.5 μM). Although VS1 has a moderate potency, we demonstrated that it specifically targets STARD3 in the cells and induces its degradation. Overall, the results confirm the reliability of the computational strategies herein applied and the identification of the first hit compound for the development of novel potent STARD3 inhibitors.

New insight into structure-activity of furan-based salicylate synthase (MbtI) inhibitors as potential antitubercular agents

Starting from the analysis of the hypothetical binding mode of our previous furan-based hit (I), we successfully achieved our objective to replace the nitro moiety, leading to the disclosure of a new lead exhibiting a strong activity against MbtI. Our best candidate 1 h displayed a K_i of 8.8 µM and its antimycobacterial activity (MIC⁹⁹ = 250 µM) is conceivably related to mycobactin biosynthesis inhibition. These results support the hypothesis that 5-phenylfuran-2-carboxylic derivatives are a promising class of MbtI inhibitors.

Computationally driven discovery of phenyl(piperazin-1-yl)methanone derivatives as reversible monoacylglycerol lipase (MAGL) inhibitors

Monoacylglycerol lipase (MAGL) is an attractive therapeutic target for many pathologies, including neurodegenerative diseases, cancer as well as chronic pain and inflammatory pathologies. The identification of reversible MAGL inhibitors, devoid of the side effects associated to prolonged MAGL inactivation, is a hot topic in medicinal chemistry. In this study, a novel phenyl(piperazin-1-yl)methanone inhibitor of MAGL was identified through a virtual screening protocol based on a fingerprint-driven consensus docking (CD) approach. Molecular modeling and preliminary structure-based hit optimization studies allowed the discovery of derivative 4, which showed an efficient reversible MAGL inhibition (IC₅₀ = 6.1 µM) and a promising antiproliferative activity on breast and ovarian cancer cell lines (IC₅₀ of 31-72 µM), thus representing a lead for the development of new and more potent reversible MAGL inhibitors. Moreover, the obtained results confirmed the reliability of the fingerprint-driven CD approach herein developed.

Extensive Reliability Evaluation of Docking-Based Target-Fishing Strategies

The development of target-fishing approaches, aimed at identifying the possible protein targets of a small molecule, represents a hot topic in medicinal chemistry. A successful target-fishing approach would allow for the elucidation of the mechanism of action of all therapeutically interesting compounds for which the actual target is still unknown. Moreover, target-fishing would be essential for preventing adverse effects of drug candidates, by predicting their potential off-targets, and it would speed up drug repurposing campaigns. However, due to the huge number of possible protein targets that a small-molecule might interact with, experimental target-fishing approaches are out of reach. In silico target-fishing represents a valuable alternative, and examples of receptor-based approaches, exploiting the large number of crystallographic protein structures determined to date, have been reported in the literature. To the best of our knowledge, no proper evaluation of such approaches is, however, reported yet. In the present work, we extensively assessed the reliability of docking-based target-fishing strategies. For this purpose, a set of X-ray structures belonging to different targets was selected, and a dataset of compounds, including 10 experimentally active ligands for each target, was created. A target-fishing benchmark database was then obtained, and used to assess the performance of 13 different docking procedures, in identifying the correct target of the dataset ligands. Moreover, a consensus docking-based target-fishing strategy was developed and evaluated. The analysis highlighted that specific features of the target proteins could affect the reliability of the protocol, which however, proved to represent a valuable tool in the proper applicability domain. Our study represents the first extensive performance assessment of docking-based target-fishing approaches, paving the way for the development of novel efficient receptor-based target fishing strategies.

Using a Consensus Docking Approach to Predict Adverse Drug Reactions in Combination Drug Therapies for Gulf War Illness

Gulf War Illness (GWI) is a chronic multisymptom illness characterized by fatigue, musculoskeletal pain, and gastrointestinal and cognitive dysfunction believed to stem from chemical exposures during the 1990⁻1991 Persian Gulf War. There are currently no treatments; however, previous studies have predicted a putative multi-intervention treatment composed of inhibiting Th1 immune cytokines followed by inhibition of the glucocorticoid receptor (GCR) to treat GWI. These predictions suggest the use of specific monoclonal antibodies or suramin to target interleukin-2 and tumor necrosis factor α , followed by mifepristone to inhibit the GCR. In addition to this putative treatment strategy, there exist a variety of medications that target GWI symptomatology. As pharmaceuticals are promiscuous molecules, binding to multiple sites beyond their intended targets, leading to off-target interactions, it is key to ensure that none of these medications interfere with the proposed treatment avenue. Here, we used the drug docking programs AutoDock 4.2, AutoDock Vina, and Schrödinger's Glide to assess the potential off-target immune and hormone interactions of 43 FDA-approved drugs commonly used to treat GWI symptoms in order to determine their putative polypharmacology and minimize adverse drug effects in a combined pharmaceutical treatment. Several of these FDA-approved drugs were predicted to be novel binders of immune and hormonal targets, suggesting caution for their use in the proposed GWI treatment strategy symptoms.

Implications of protein conformations to modifying novel inhibitor Oseltamivir for 2009 H1N1 influenza A virus by simulation and docking studies

Recently three FDA approved existing drugs, namely-Oseltamivir, Peramivir and Zanamivir, used against Neuraminidase (NA) for the inhibitory effect on the process of viral progeny release to inhibit infection. All NA subtypes has been divided into two groups (Group 1 and Group 2) based on phylogenetic study. Oseltamivir and Zanamivir drugs are designed for Group 2 NA but are also used against 2009 H1N1 NA that lies in Group 1. There is no specific drug available for H1N1 and, consequently, there is an urgent requirement for the same. The structure-based drug design and fragment-based drug design methods are used for building more effective and economic drug molecules. In this work, the fragment-based drug development followed by fragment evolution on the basis of protein conformations after every 10 ns of 100 ns simulation. There are two analogs of Oseltamivir acid drug discovered in this study. Only analog 1, along with Oseltamivir acid, were then docked with the native protein. The analog 1 (benzoic acid inhibitor 11) exhibited higher binding affinity value of - 10.70 kcal/mol in comparison to its predecessor. The concept of conformations and protein-ligand interactions can be useful in designing new drugs for H1N1 with high specific binding.

In silico Description of LAT1 Transport Mechanism at an Atomistic Level

The molecular mechanism of transport mediated by LAT1, a sodium-independent antiporter of large neutral amino acids, was investigated through in silico procedures, specifically making reference to two transported substrates, tyrosine (Tyr) and leucine methyl ester (LME), and to 3,5-diiodo-L-tyrosine (DIT), a well-known LAT1 inhibitor. Two models of the transporter were built by comparative modeling, with LAT1 either in an outward-facing (OF) or in an inward-facing (IF) conformation, based, respectively, on the crystal structure of AdiC and of GadC. As frequently classic Molecular Dynamics (MD) fails to monitor large-scale conformational transitions within a reasonable simulated time, the OF structure was equilibrated for 150 ns then processed through targeted MD (tMD). During this procedure, an elastic force pulled the OF structure to the IF structure and induced, at the same time, substrates/inhibitor to move through the transport channel. This elastic force was modulated by a spring constant (k) value; by decreasing its value from 100 to 70, it was possible to comparatively account for the propensity for transport of the three tested molecules. In line with our expectations, during the tMD simulations, Tyr and LME behaved as substrates, moving down the transport channel, or most of it, for all k values. On the contrary, DIT behaved as an inhibitor, being (almost) transported across the channel only at the highest k value (100). During their transit through the channel, Tyr and LME interacted with specific amino acids (first with Phe252 then with Thr345, Arg348, Tyr259, and Phe262); this suggests that a primary as well as a putative secondary gate may contribute to the transport of substrates. Quite on the opposite, DIT appeared to establish only transient interactions with side chains lining the external part of the transport channel. Our tMD simulations could thus efficiently discriminate between two transported substrates and one inhibitor, and therefore can be proposed as a benchmark for developing novel LAT1 inhibitors of pharmacological interest.

Development of a Fingerprint-Based Scoring Function for the Prediction of the Binding Mode of Carbonic Anhydrase II Inhibitors

Carbonic anhydrase II (CAII) is a zinc-containing metalloenzyme whose aberrant activity is associated with various diseases such as glaucoma, osteoporosis, and different types of tumors; therefore, the development of CAII inhibitors, which can represent promising therapeutic agents for the treatment of these pathologies, is a current topic in medicinal chemistry. Molecular docking is a commonly used tool in structure-based drug design of enzyme inhibitors. However, there is still a need for improving docking reliability, especially in terms of scoring functions, since the complex pattern of energetic contributions driving ligand–protein binding cannot be properly described by mathematical functions only including approximated energetic terms. Here we report a novel CAII-specific fingerprint-based (IFP) scoring function developed according to the ligand–protein interactions detected in the CAII-inhibitor co-crystal structures of the most potent CAII ligands. Our IFP scoring function outperformed the ability of Autodock4 scoring function to identify native-like docking poses of CAII inhibitors and thus allowed a considerable improvement of docking reliability. Moreover, the ligand–protein interaction fingerprints showed a useful application in the binding mode analysis of structurally diverse CAII ligands.

New Chromane-Based Derivatives as Inhibitors of Mycobacterium tuberculosis Salicylate Synthase (MbtI): Preliminary Biological Evaluation and Molecular Modeling Studies

Tuberculosis is the leading cause of death from a single infectious agent worldwide; therefore, the need for new antitubercular drugs is desperate. The recently validated target salicylate synthase MbtI is the first enzyme involved in the biosynthesis of mycobactins, compounds able to chelate iron, an essential cofactor for the survival of Mycobacterium tuberculosis in the host. Here, we report on the synthesis and biological evaluation of chromane-based compounds as new potential inhibitors of MbtI. Our approach successfully allowed the identification of a novel lead compound (1), endowed with a promising activity against this enzyme (IC₅₀ = 55 μM). Molecular modeling studies were performed in order to evaluate the binding mode of 1 and rationalize the preliminary structure-activity relationships, thus providing crucial information to carry out further optimization studies.

Binding investigation and preliminary optimisation of the 3-amino-1,2,4-triazin-5(2H)-one core for the development of new Fyn inhibitors

Fyn tyrosine kinase inhibitors are considered potential therapeutic agents for a variety of human cancers. Furthermore, the involvement of Fyn kinase in signalling pathways that lead to severe pathologies, such as Alzheimer's and Parkinson's diseases, has also been demonstrated. In this study, starting from 3-(benzo[d][1,3]dioxol-5-ylamino)-6-methyl-1,2,4-triazin-5(2H)-one (VS6), a hit compound that showed a micromolar inhibition of Fyn (IC₅₀ = 4.8 μM), we computationally investigated the binding interactions of the 3-amino-1,2,4-triazin-5(2H)-one scaffold and started a preliminary hit to lead optimisation. This analysis led us to confirm the hypothesised binding mode of VS6 and to identify a new derivative that is about 6-fold more active than VS6 (compound 3, IC₅₀ = 0.76 μM).

Discovery and development of novel salicylate synthase (MbtI) furanic inhibitors as antitubercular agents

We report on the virtual screening, synthesis, and biological evaluation of new furan derivatives targeting Mycobacterium tuberculosis salicylate synthase (MbtI). A receptor-based virtual screening procedure was applied to screen the Enamine database, identifying two compounds, I and III, endowed with a good enzyme inhibitory activity. Considering the most active compound I as starting point for the development of novel MbtI inhibitors, we obtained new derivatives based on the furan scaffold. Among the SAR performed on this class, compound 1a emerged as the most potent MbtI inhibitor reported to date (K_i = 5.3 μM). Moreover, compound 1a showed a promising antimycobacterial activity (MIC⁹⁹ = 156 μM), which is conceivably related to mycobactin biosynthesis inhibition.

Rational Development of MAGL Inhibitors

Hit identification and hit-to-lead optimization are key steps of the early drug discovery program. Starting from the X-ray crystal structure of the human monoacylglycerol lipase (hMAGL), we herein describe the computational and experimental procedures that we applied for identifying and optimizing a new active inhibitor of this target enzyme. A receptor-based virtual screening method is reported in details, together with enzymatic assays and a first round of hit optimization.

Development of terphenyl-2-methyloxazol-5(4H)-one derivatives as selective reversible MAGL inhibitors

Monoacylglycerol lipase is a serine hydrolase that plays a major role in the degradation of the endocannabinoid neurotransmitter 2-arachidonoylglycerol. A wide number of MAGL inhibitors are reported in literature; however, many of them are characterised by an irreversible mechanism of action and this behavior determines an unwanted chronic MAGL inactivation, which acquires a functional antagonism of the endocannabinoid system. The possible use of reversible MAGL inhibitors has only recently been explored, due to the lack of known compounds possessing efficient reversible inhibitory activities. In this work, we report a new series of terphenyl-2-methyloxazol-5(4H)-one derivatives characterised by a reversible MAGL-inhibition mechanism. Among them, compound 20b showed to be a potent MAGL reversible inhibitor (IC₅₀ = 348 nM) with a good MAGL/FAAH selectivity. Furthermore, this compound showed antiproliferative activities against two different cancer cell lines that overexpress MAGL.

Machine Learning Consensus Scoring Improves Performance Across Targets in Structure-Based Virtual Screening

In structure-based virtual screening, compound ranking through a consensus of scores from a variety of docking programs or scoring functions, rather than ranking by scores from a single program, provides better predictive performance and reduces target performance variability. Here we compare traditional consensus scoring methods with a novel, unsupervised gradient boosting approach. We also observed increased score variation among active ligands and developed a statistical mixture model consensus score based on combining score means and variances. To evaluate performance, we used the common performance metrics ROCAUC and EF1 on 21 benchmark targets from DUD-E. Traditional consensus methods, such as taking the mean of quantile normalized docking scores, outperformed individual docking methods and are more robust to target variation. The mixture model and gradient boosting provided further improvements over the traditional consensus methods. These methods are readily applicable to new targets in academic research and overcome the potentially poor performance of using a single docking method on a new target.