Scipion-Chem: An Open Platform for Virtual Drug Screening.

Virtual drug screening (VDS) tackles the problem of drug discovery by computationally reducing the number of potential pharmacological molecules that need to be tested experimentally to find a new drug. To do so, several approaches have been developed through the years, typically focusing on either the physicochemical characteristics of the receptor structure (structure-based virtual screening) or those of the potential ligands (ligand-based virtual screening). Scipion is a workflow engine well suited for structural studies of biological macromolecules. Here, we present Scipion-chem, a new branch oriented to VDS. A total of 11 plugins have already been integrated from the most common programs used in the field. They can be used through the Scipion graphical user interface to execute and analyze typical VDS tasks. In addition, we have developed several consensus protocols that combine results from the different integrated programs to generate more robust predictions. Backstage, Scipion also facilitates the interoperability of those different software packages while tracking all of the intermediate files, parameters, and user decisions. In summary, in this article, we present Scipion-chem. This accessible, interoperable, and traceable platform provides the user with all of the tools to carry out a successful VDS workflow. Scipion-chem is openly available at https://github.com/scipion-chem.

Multitask Deep Neural Networks for Ames Mutagenicity Prediction.

The Ames mutagenicity test constitutes the most frequently used assay to estimate the mutagenic potential of drug candidates. While this test employs experimental results using various strains of Salmonella typhimurium, the vast majority of the published in silico models for predicting mutagenicity do not take into account the test results of the individual experiments conducted for each strain. Instead, such QSAR models are generally trained employing overall labels (i.e., mutagenic and nonmutagenic). Recently, neural-based models combined with multitask learning strategies have yielded interesting results in different domains, given their capabilities to model multitarget functions. In this scenario, we propose a novel neural-based QSAR model to predict mutagenicity that leverages experimental results from different strains involved in the Ames test by means of a multitask learning approach. To the best of our knowledge, the modeling strategy hereby proposed has not been applied to model Ames mutagenicity previously. The results yielded by our model surpass those obtained by single-task modeling strategies, such as models that predict the overall Ames label or ensemble models built from individual strains. For reproducibility and accessibility purposes, all source code and datasets used in our experiments are publicly available.

Multitarget drugs as potential therapeutic agents for alzheimer's disease. A new family of 5-substituted indazole derivatives as cholinergic and BACE1 inhibitors.

Multitarget drugs are a promising therapeutic approach against Alzheimer's disease. In this work, a new family of 5-substituted indazole derivatives with a multitarget profile including cholinesterase and BACE1 inhibition is described. Thus, the synthesis and evaluation of a new class of 5-substituted indazoles has been performed. Pharmacological evaluation includes in vitro inhibitory assays on AChE/BuChE and BACE1 enzymes. Also, the corresponding competition studies on BuChE were carried out. Additionally, antioxidant properties have been calculated from ORAC assays. Furthermore, studies of anti-inflammatory properties on Raw 264.7 cells and neuroprotective effects in human neuroblastoma SH-SY5Y cells have been performed. The results of pharmacological tests have shown that some of these 5-substituted indazole derivatives 1-4 and 6 behave as AChE/BuChE and BACE1 inhibitors, simultaneously. In addition, some indazole derivatives showed anti-inflammatory (3, 6) and neuroprotective (1-4 and 6) effects against Aß-induced cell death in human neuroblastoma SH-SY5Y cells with antioxidant properties.

Discovery of Amoebicidal Compounds by Combining Computational and Experimental Approaches.

Pathogenic and opportunistic free-living amoebae such as Acanthamoeba spp. can cause keratitis (Acanthamoeba keratitis [AK]), which may ultimately lead to permanent visual impairment or blindness. Acanthamoeba can also cause rare but usually fatal granulomatous amoebic encephalitis (GAE). Current therapeutic options for AK require a lengthy treatment with nonspecific drugs that are often associated with adverse effects. Recent developments in the field led us to target cAMP pathways, specifically phosphodiesterase. Guided by computational tools, we targeted the Acanthamoeba phosphodiesterase RegA. Computational studies led to the construction and validation of a homology model followed by a virtual screening protocol guided by induced-fit docking and chemical scaffold analysis using our medicinal and biological chemistry (MBC) chemical library. Subsequently, 18 virtual screening hits were prioritized for further testing in vitro against Acanthamoeba castellanii, identifying amoebicidal hits containing piperidine and urea imidazole cores. Promising activities were confirmed in the resistant cyst form of the amoeba and in additional clinical Acanthamoeba strains, increasing their therapeutic potential. Mechanism-of-action studies revealed that these compounds produce apoptosis through reactive oxygen species (ROS)-mediated mitochondrial damage. These chemical families show promise for further optimization to produce effective antiacanthamoebal drugs.

AI in drug development: a multidisciplinary perspective.

The introduction of a new drug to the commercial market follows a complex and long process that typically spans over several years and entails large monetary costs due to a high attrition rate. Because of this, there is an urgent need to improve this process using innovative technologies such as artificial intelligence (AI). Different AI tools are being applied to support all four steps of the drug development process (basic research for drug discovery; pre-clinical phase; clinical phase; and postmarketing). Some of the main tasks where AI has proven useful include identifying molecular targets, searching for hit and lead compounds, synthesising drug-like compounds and predicting ADME-Tox. This review, on the one hand, brings in a mathematical vision of some of the key AI methods used in drug development closer to medicinal chemists and, on the other hand, brings the drug development process and the use of different models closer to mathematicians. Emphasis is placed on two aspects not mentioned in similar surveys, namely, Bayesian approaches and their applications to molecular modelling and the eventual final use of the methods to actually support decisions. Promoting a perfect synergy.

Virtual Screening of FDA-Approved Drugs against Triose Phosphate Isomerase from Entamoeba histolytica and Giardia lamblia Identifies Inhibitors of Their Trophozoite Growth Phase.

Infectious diseases caused by intestinal protozoan, such as Entamoeba histolytica (E. histolytica) and Giardia lamblia (G. lamblia) are a worldwide public health issue. They affect more than 70 million people every year. They colonize intestines causing primarily diarrhea; nevertheless, these infections can lead to more serious complications. The treatment of choice, metronidazole, is in doubt due to adverse effects and resistance. Therefore, there is a need for new compounds against these parasites. In this work, a structure-based virtual screening of FDA-approved drugs was performed to identify compounds with antiprotozoal activity. The glycolytic enzyme triosephosphate isomerase, present in both E. histolytica and G. lamblia, was used as the drug target. The compounds with the best average docking score on both structures were selected for the in vitro evaluation. Three compounds, chlorhexidine, tolcapone, and imatinib, were capable of inhibit growth on G. lamblia trophozoites (0.05-4.935 µg/mL), while folic acid showed activity against E. histolytica (0.186 µg/mL) and G. lamblia (5.342 µg/mL).

New cannabinoid receptor antagonists as pharmacological tool.

Synthesis and pharmacological evaluation of a new series of cannabinoid receptor antagonists of indazole ether derivatives have been performed. Pharmacological evaluation includes radioligand binding assays with [3H]-CP55940 for CB1 and CB2 receptors and functional activity for cannabinoid receptors on isolated tissue. In addition, functional activity of the two synthetic cannabinoids antagonists 18 (PGN36) and 17 (PGN38) were carried out in the osteoblastic cell line MC3T3-E1 that is able to express CB2R upon osteogenic conditions. Both antagonists abolished the increase in collagen type I gene expression by the well-known inducer of bone activity, the HU308 agonist. The results of pharmacological tests have revealed that four of these derivatives behave as CB2R cannabinoid antagonists. In particular, the compounds 17 (PGN38) and 18 (PGN36) highlight as promising candidates as pharmacological tools.

Towards discovery of new leishmanicidal scaffolds able to inhibit Leishmania GSK-3.

Previous reports have validated the glycogen synthase kinase-3 (GSK-3) as a druggable target against the human protozoan parasite Leishmania. This prompted us to search for new leishmanicidal scaffolds as inhibitors of this enzyme from our in-house library of human GSK-3ß inhibitors, as well as from the Leishbox collection of leishmanicidal compounds developed by GlaxoSmithKline. As a result, new leishmanicidal inhibitors acting on Leishmania GSK-3 at micromolar concentrations were found. These inhibitors belong to six different chemical classes (thiadiazolidindione, halomethylketone, maleimide, benzoimidazole, N-phenylpyrimidine-2-amine and oxadiazole). In addition, the binding mode of the most active compounds into Leishmania GSK-3 was approached using computational tools. On the whole, we have uncovered new chemical scaffolds with an appealing prospective in the development and use of Leishmania GSK-3 inhibitors against this infectious protozoan.

Deciphering the enzymatic target of a new family of antischistosomal agents bearing a quinazoline scaffold using complementary computational tools.

A previous phenotypic screening campaign led to the identification of a quinazoline derivative with promising in vitro activity against Schistosoma mansoni. Follow-up studies of the antischistosomal potential of this candidate are presented here. The in vivo studies in a S. mansoni mouse model show a significant reduction of total worms and a complete disappearance of immature eggs when administered concomitantly with praziquantel in comparison with the administration of praziquantel alone. This fact is of utmost importance because eggs are responsible for the pathology and transmission of the disease. Subsequently, the chemical optimisation of the structure in order to improve the metabolic stability of the parent compound was carried out leading to derivatives with improved drug-like properties. Additionally, the putative target of this new class of antischistosomal compounds was envisaged by using computational tools and the binding mode to the target enzyme, aldose reductase, was proposed.

Interference of the complex between NCS-1 and Ric8a with phenothiazines regulates synaptic function and is an approach for fragile X syndrome.

The protein complex formed by the Ca2+ sensor neuronal calcium sensor 1 (NCS-1) and the guanine exchange factor protein Ric8a coregulates synapse number and probability of neurotransmitter release, emerging as a potential therapeutic target for diseases affecting synapses, such as fragile X syndrome (FXS), the most common heritable autism disorder. Using crystallographic data and the virtual screening of a chemical library, we identified a set of heterocyclic small molecules as potential inhibitors of the NCS-1/Ric8a interaction. The aminophenothiazine FD44 interferes with NCS-1/Ric8a binding, and it restores normal synapse number and associative learning in a Drosophila FXS model. The synaptic effects elicited by FD44 feeding are consistent with the genetic manipulation of NCS-1. The crystal structure of NCS-1 bound to FD44 and the structure-function studies performed with structurally close analogs explain the FD44 specificity and the mechanism of inhibition, in which the small molecule stabilizes a mobile C-terminal helix inside a hydrophobic crevice of NCS-1 to impede Ric8a interaction. Our study shows the drugability of the NCS-1/Ric8a interface and uncovers a suitable region in NCS-1 for development of additional drugs of potential use on FXS and related synaptic disorders.

Computational Drug Repositioning for Chagas Disease Using Protein-Ligand Interaction Profiling.

Chagas disease, caused by Trypanosoma cruzi (T. cruzi), affects nearly eight million people worldwide. There are currently only limited treatment options, which cause several side effects and have drug resistance. Thus, there is a great need for a novel, improved Chagas treatment. Bifunctional enzyme dihydrofolate reductase-thymidylate synthase (DHFR-TS) has emerged as a promising pharmacological target. Moreover, some human dihydrofolate reductase (HsDHFR) inhibitors such as trimetrexate also inhibit T. cruzi DHFR-TS (TcDHFR-TS). These compounds serve as a starting point and a reference in a screening campaign to search for new TcDHFR-TS inhibitors. In this paper, a novel virtual screening approach was developed that combines classical docking with protein-ligand interaction profiling to identify drug repositioning opportunities against T. cruzi infection. In this approach, some food and drug administration (FDA)-approved drugs that were predicted to bind with high affinity to TcDHFR-TS and whose predicted molecular interactions are conserved among known inhibitors were selected. Overall, ten putative TcDHFR-TS inhibitors were identified. These exhibited a similar interaction profile and a higher computed binding affinity, compared to trimetrexate. Nilotinib, glipizide, glyburide and gliquidone were tested on T. cruzi epimastigotes and showed growth inhibitory activity in the micromolar range. Therefore, these compounds could lead to the development of new treatment options for Chagas disease.

Highly potent and selective aryl-1,2,3-triazolyl benzylpiperidine inhibitors toward butyrylcholinesterase in Alzheimer's disease.

Acetylcholinesterase (AChE) is the key enzyme targeted in Alzheimer's disease (AD) therapy, nevertheless butyrylcholinesterase (BuChE) has been drawing attention due to its role in the disease progression. Thus, we aimed to synthesize novel cholinesterases inhibitors considering structural differences in their peripheral site, exploiting a moiety replacement approach based on the potent and selective hAChE drug donepezil. Hence, two small series of N-benzylpiperidine based compounds have successfully been synthesized as novel potent and selective hBuChE inhibitors. The most promising compounds (9 and 11) were not cytotoxic and their kinetic study accounted for dual binding site mode of interaction, which is in agreement with further docking and molecular dynamics studies. Therefore, this study demonstrates how our strategy enabled the discovery of novel promising and privileged structures. Remarkably, compound 11 proved to be one of the most potent (0.17 nM) and selective (>58,000-fold) hBuChE inhibitor ever reported.

Computer-aided molecular design of pyrazolotriazines targeting glycogen synthase kinase 3.

Numerous studies have highlighted the implications of the glycogen synthase kinase 3 (GSK-3) in several processes associated with Alzheimer's disease (AD). Therefore, GSK-3 has become a crucial therapeutic target for the treatment of this neurodegenerative disorder. Hereby, we report the design and multistep synthesis of ethyl 4-oxo-pyrazolo[4,3-d][1-3]triazine-7-carboxylates and their biological evaluation as GSK-3 inhibitors. Molecular modelling studies allow us to develop this new scaffold optimising the chemical structure. Potential binding mode determination in the enzyme and the analysis of the key features in the catalytic site are also described. Furthermore, the ability of pyrazolotriazinones to cross the blood-brain barrier (BBB) was evaluated by passive diffusion and those who showed great GSK-3 inhibition and permeation to the central nervous system (CNS) showed neuroprotective properties against tau hyperphosphorylation in a cell-based model. These new brain permeable pyrazolotriazinones may be used for key in vivo studies and may be considered as new leads for further optimisation for the treatment of AD.

Cyclic Nucleotide-Specific Phosphodiesterases as Potential Drug Targets for Anti-Leishmania Therapy.

The available treatments for leishmaniasis are less than optimal due to inadequate efficacy, toxic side effects, and the emergence of resistant strains, clearly endorsing the urgent need for discovery and development of novel drug candidates. Ideally, these should act via an alternative mechanism of action to avoid cross-resistance with the current drugs. As cyclic nucleotide-specific phosphodiesterases (PDEs) of Leishmania major have been postulated as putative drug targets, a series of potential inhibitors of Leishmania PDEs were explored. Several displayed potent and selective in vitro activity against L. infantum intracellular amastigotes. One imidazole derivative, compound 35, was shown to reduce the parasite loads in vivo and to increase the cellular cyclic AMP (cAMP) level at in a dose-dependent manner at just 2× and 5× the 50% inhibitory concentration (IC50), indicating a correlation between antileishmanial activity and increased cellular cAMP levels. Docking studies and molecular dynamics simulations pointed to imidazole 35 exerting its activity through PDE inhibition. This study establishes for the first time that inhibition of cAMP PDEs can potentially be exploited for new antileishmanial chemotherapy.

Identification of new allosteric sites and modulators of AChE through computational and experimental tools.

Allosteric sites on proteins are targeted for designing more selective inhibitors of enzyme activity and to discover new functions. Acetylcholinesterase (AChE), which is most widely known for the hydrolysis of the neurotransmitter acetylcholine, has a peripheral allosteric subsite responsible for amyloidosis in Alzheimer's disease through interaction with amyloid ß-peptide. However, AChE plays other non-hydrolytic functions. Here, we identify and characterise using computational tools two new allosteric sites in AChE, which have allowed us to identify allosteric inhibitors by virtual screening guided by structure-based and fragment hotspot strategies. The identified compounds were also screened for in vitro inhibition of AChE and three were observed to be active. Further experimental (kinetic) and computational (molecular dynamics) studies have been performed to verify the allosteric activity. These new compounds may be valuable pharmacological tools in the study of non-cholinergic functions of AChE.

Medicinal and Biological Chemistry (MBC) Library: An Efficient Source of New Hits.

Identification of new hits is one of the biggest challenges in drug discovery. Creating a library of well-characterized drug-like compounds is a key step in this process. Our group has developed an in-house chemical library called the Medicinal and Biological Chemistry (MBC) library. This collection has been successfully used to start several medicinal chemistry programs and developed in an accumulation of more than 30 years of experience in drug design and discovery of new drugs for unmet diseases. It contains over 1000 compounds, mainly heterocyclic scaffolds. In this work, analysis of drug-like properties and comparative study with well-known libraries by using different computer software are presented here.

Benzodioxane-Benzamides as FtsZ Inhibitors: Effects of Linker's Functionalization on Gram-Positive Antimicrobial Activity.

FtsZ is an essential bacterial protein abundantly studied as a novel and promising target for antimicrobials. FtsZ is highly conserved among bacteria and mycobacteria, and it is crucial for the correct outcome of the cell division process, as it is responsible for the division of the parent bacterial cell into two daughter cells. In recent years, the benzodioxane-benzamide class has emerged as very promising and capable of targeting both Gram-positive and Gram-negative FtsZs. In this study, we explored the effect of including a substituent on the ethylenic linker between the two main moieties on the antimicrobial activity and pharmacokinetic properties. This substitution, in turn, led to the generation of a second stereogenic center, with both erythro and threo isomers isolated, characterized, and evaluated. With this work, we discovered how the hydroxy group slightly affects the antimicrobial activity, while being an important anchor for the exploitation and development of prodrugs, probes, and further derivatives.

Design of New Dispersants Using Machine Learning and Visual Analytics.

Artificial intelligence (AI) is an emerging technology that is revolutionizing the discovery of new materials. One key application of AI is virtual screening of chemical libraries, which enables the accelerated discovery of materials with desired properties. In this study, we developed computational models to predict the dispersancy efficiency of oil and lubricant additives, a critical property in their design that can be estimated through a quantity named blotter spot. We propose a comprehensive approach that combines machine learning techniques with visual analytics strategies in an interactive tool that supports domain experts' decision-making. We evaluated the proposed models quantitatively and illustrated their benefits through a case study. Specifically, we analyzed a series of virtual polyisobutylene succinimide (PIBSI) molecules derived from a known reference substrate. Our best-performing probabilistic model was Bayesian Additive Regression Trees (BART), which achieved a mean absolute error of 5.50±0.34 and a root mean square error of 7.56±0.47, as estimated through 5-fold cross-validation. To facilitate future research, we have made the dataset, including the potential dispersants used for modeling, publicly available. Our approach can help accelerate the discovery of new oil and lubricant additives, and our interactive tool can aid domain experts in making informed decisions based on blotter spot and other key properties.

N'-Phenylacetohydrazide Derivatives as Potent Ebola Virus Entry Inhibitors with an Improved Pharmacokinetic Profile.

Ebola virus (EBOV) is a single-strand RNA virus belonging to the Filoviridae family, which has been associated to most Ebola virus disease outbreaks to date, including the West African and the North Kivu epidemics between 2013 and 2022. This unprecedented health emergency prompted the search for effective medical countermeasures. Following up on the carbazole hit identified in our previous studies, we synthetized a new series of compounds, which demonstrated to prevent EBOV infection in cells by acting as virus entry inhibitors. The in vitro inhibitory activity was evaluated through the screening against surrogate models based on viral pseudotypes and further confirmed using replicative EBOV. Docking and molecular dynamics simulations joined to saturation transfer difference-nuclear magnetic resonance (STD-NMR) and mutagenesis experiments to elucidate the biological target of the most potent compounds. Finally, in vitro metabolic stability and in vivo pharmacokinetic studies were performed to confirm their therapeutic potential.

In Silico Analysis of Potential Drug Targets for Protozoan Infections.

BACKGROUND: Currently, protozoan infectious diseases affect billions of people every year. Their pharmacological treatments offer few alternatives and are restrictive due to undesirable side effects and parasite drug resistance. OBJECTIVE: In this work, three ontology-based approaches were used to identify shared potential drug targets in five species of protozoa. METHODS: In this study, proteomes of five species of protozoa: Entamoeba histolytica (E. histolytica), Giardia lamblia (G. lamblia), Trichomonas vaginalis (T. vaginalis), Trypanosoma cruzi (T. cruzi), and Leishmania mexicana (L. mexicana), were compared through orthology inference using three different tools to identify potential drug targets. RESULTS: Comparing the proteomes of E. histolytica, G. lamblia, T. vaginalis, T. cruzi, and L. mexicana, twelve targets for developing new drugs with antiprotozoal activity were identified. CONCLUSION: New drug targets were identified by orthology-based analysis; therefore, they could be considered for the development of new broad-spectrum antiprotozoal drugs. Particularly, triosephosphate isomerase emerges as a common target in trypanosomatids and amitochondriate parasites.