Identification of two novel chemical classes of Autotaxin (ATX) inhibitors using Enalos Asclepios KNIME nodes.

Autotaxin is a secreted lysophospholipase D which is a member of the ectonucleotide pyrophosphatase/phosphodiesterase family converting extracellular lysophosphatidylcholine and other non-choline lysophospholipids, such as lysophosphatidylethanolamine and lysophosphatidylserine, to the lipid mediator lysophosphatidic acid. Autotaxin is implicated in various fibroproliferative diseases including interstitial lung diseases, such as idiopathic pulmonary fibrosis and hepatic fibrosis, as well as in cancer. In this study, we present an effort of identifying ATX inhibitors that bind to allosteric ATX binding sites using the Enalos Asclepios KNIME Node. All the available PDB crystal structures of ATX were collected, prepared, and aligned. Visual examination of these structures led to the identification of four crystal structures of human ATX co-crystallized with four known inhibitors. These inhibitors bind to five binding sites with five different binding modes. These five binding sites were thereafter used to virtually screen a compound library of 14,000 compounds to identify molecules that bind to allosteric sites. Based on the binding mode and interactions, the docking score, and the frequency that a compound comes up as a top-ranked among the five binding sites, 24 compounds were selected for in vitro testing. Finally, two compounds emerged with inhibitory activity against ATX in the low micromolar range, while their mode of inhibition and binding pattern were also studied. The two derivatives identified herein can serve as "hits" towards developing novel classes of ATX allosteric inhibitors.

Application of Machine Learning in Nanotoxicology: A Critical Review and Perspective.

The massive production and application of nanomaterials (NMs) have raised concerns about the potential adverse effects of NMs on human health and the environment. Evaluating the adverse effects of NMs by laboratory methods is expensive, time-consuming, and often fails to keep pace with the invention of new materials. Therefore, in silico methods that utilize machine learning techniques to predict the toxicity potentials of NMs are a promising alternative approach if regulatory confidence in them can be enhanced. Previous reviews and regulatory OECD guidance documents have discussed in detail how to build an in silico predictive model for NMs. Nevertheless, there is still room for improvement in addressing the ways to enhance the model representativeness and performance from different angles, such as data set curation, descriptor selection, task type (classification/regression), algorithm choice, and model evaluation (internal and external validation, applicability domain, and mechanistic interpretation, which is key to ensuring stakeholder confidence). This review explores how to build better predictive models; the current state of the art is analyzed via a statistical evaluation of literature, while the challenges faced and future perspectives are summarized. Moreover, a recommended workflow and best practices are provided to help in developing more predictive, reliable, and interpretable models that can assist risk assessment as well as safe-by-design development of NMs.

Development of a Robust Read-Across Model for the Prediction of Biological Potency of Novel Peroxisome Proliferator-Activated Receptor Delta Agonists.

A robust predictive model was developed using 136 novel peroxisome proliferator-activated receptor delta (PPARδ) agonists, a distinct subtype of lipid-activated transcription factors of the nuclear receptor superfamily that regulate target genes by binding to characteristic sequences of DNA bases. The model employs various structural descriptors and docking calculations and provides predictions of the biological activity of PPARδ agonists, following the criteria of the Organization for Economic Co-operation and Development (OECD) for the development and validation of quantitative structure-activity relationship (QSAR) models. Specifically focused on small molecules, the model facilitates the identification of highly potent and selective PPARδ agonists and offers a read-across concept by providing the chemical neighbours of the compound under study. The model development process was conducted on Isalos Analytics Software (v. 0.1.17) which provides an intuitive environment for machine-learning applications. The final model was released as a user-friendly web tool and can be accessed through the Enalos Cloud platform's graphical user interface (GUI).

Systematic Review of Naturally Derived Substances That Act as Inhibitors of the Nicotine Metabolizing Enzyme Cytochrome P450 2A6.

Tobacco smoking has been highlighted as a major health challenge in modern societies. Despite not causing death directly, smoking has been associated with several health issues, such as cardiovascular diseases, respiratory disorders, and several cancer types. Moreover, exposure to nicotine during pregnancy has been associated with adverse neurological disorders in babies. Nicotine Replacement Therapy (NRT) is the most common strategy employed for smoking cessation, but despite its widespread use, NRT presents with low success and adherence rates. This is attributed partially to the rate of nicotine metabolism by cytochrome P450 2A6 (CYP2A6) in each individual. Nicotine addiction is correlated with the high rate of its metabolism, and thus, novel strategies need to be implemented in NRT protocols. Naturally derived products are a cost-efficient and rich source for potential inhibitors, with the main advantages being their abundance and ease of isolation. This systematic review aims to summarize the natural products that have been identified as CYP2A6 inhibitors, validated through in vitro and/or in vivo assays, and could be implemented as nicotine metabolism inhibitors. The scope is to present the different compounds and highlight their possible implementation in NRT strategies. Additionally, this information would provide valuable insight regarding CYP2A6 inhibitors, that can be utilized in drug development via the use of in silico methodologies and machine-learning models to identify new potential lead compounds for optimization and implementation in NRT regimes.

Discovery of the First-in-Class Inhibitors of Hypoxia Up-Regulated Protein†1 (HYOU1) Suppressing Pathogenic Fibroblast Activation.

Fibroblasts are key regulators of inflammation, fibrosis, and cancer. Targeting their activation in these complex diseases has emerged as a novel strategy to restore tissue homeostasis. Here, we present a multidisciplinary lead discovery approach to identify and optimize small molecule inhibitors of pathogenic fibroblast activation. The study encompasses medicinal chemistry, molecular phenotyping assays, chemoproteomics, bulk RNA-sequencing analysis, target validation experiments, and chemical absorption, distribution, metabolism, excretion and toxicity (ADMET)/pharmacokinetic (PK)/in vivo evaluation. The parallel synthesis employed for the production of the new benzamide derivatives enabled us to a) pinpoint key structural elements of the scaffold that provide potent fibroblast-deactivating effects in cells, b) discriminate atoms or groups that favor or disfavor a desirable ADMET profile, and c) identify metabolic "hot spots". Furthermore, we report the discovery of the first-in-class inhibitor leads for hypoxia up-regulated protein 1 (HYOU1), a member of the heat shock protein 70 (HSP70) family often associated with cellular stress responses, particularly under hypoxic conditions. Targeting HYOU1 may therefore represent a potentially novel strategy to modulate fibroblast activation and treat chronic inflammatory and fibrotic disorders.

A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation.

The discovery and development of new drugs are extremely long and costly processes. Recent progress in artificial intelligence has made a positive impact on the drug development pipeline. Numerous challenges have been addressed with the growing exploitation of drug-related data and the advancement of deep learning technology. Several model frameworks have been proposed to enhance the performance of deep learning algorithms in molecular design. However, only a few have had an immediate impact on drug development since computational results may not be confirmed experimentally. This systematic review aims to summarize the different deep learning architectures used in the drug discovery process and are validated with further in vivo experiments. For each presented study, the proposed molecule or peptide that has been generated or identified by the deep learning model has been biologically evaluated in animal models. These state-of-the-art studies highlight that even if artificial intelligence in drug discovery is still in its infancy, it has great potential to accelerate the drug discovery cycle, reduce the required costs, and contribute to the integration of the 3R (Replacement, Reduction, Refinement) principles. Out of all the reviewed scientific articles, seven algorithms were identified: recurrent neural networks, specifically, long short-term memory (LSTM-RNNs), Autoencoders (AEs) and their Wasserstein Autoencoders (WAEs) and Variational Autoencoders (VAEs) variants; Convolutional Neural Networks (CNNs); Direct Message Passing Neural Networks (D-MPNNs); and Multitask Deep Neural Networks (MTDNNs). LSTM-RNNs were the most used architectures with molecules or peptide sequences as inputs.

Exploring the Binding Effects of Natural Products and Antihypertensive Drugs on SARS-CoV-2: An In Silico Investigation of Main Protease and Spike Protein.

In this in silico study, we conducted an in-depth exploration of the potential of natural products and antihypertensive molecules that could serve as inhibitors targeting the key proteins of the SARS-CoV-2 virus: the main protease (Mpro) and the spike (S) protein. By utilizing Induced Fit Docking (IFD), we assessed the binding affinities of the molecules under study to these crucial viral components. To further comprehend the stability and molecular interactions of the "protein-ligand" complexes that derived from docking studies, we performed molecular dynamics (MD) simulations, shedding light on the molecular basis of potential drug candidates for COVID-19 treatment. Moreover, we employed Molecular Mechanics Generalized Born Surface Area (MM-GBSA) calculations on all "protein-ligand" complexes, underscoring the robust binding capabilities of rosmarinic acid, curcumin, and quercetin against Mpro, and salvianolic acid b, rosmarinic acid, and quercetin toward the S protein. Furthermore, in order to expand our search for potent inhibitors, we conducted a structure similarity analysis, using the Enalos Suite, based on the molecules that indicated the most favored results in the in silico studies. The Enalos Suite generated 115 structurally similar compounds to salvianolic acid, rosmarinic acid, and quercetin. These compounds underwent IFD calculations, leading to the identification of two salvianolic acid analogues that exhibited strong binding to all the examined binding sites in both proteins, showcasing their potential as multi-target inhibitors. These findings introduce exciting possibilities for the development of novel therapeutic agents aiming to effectively disrupt the SARS-CoV-2 virus lifecycle.

Precision Medicine Approaches with Metabolomics and Artificial Intelligence.

Recent technological innovations in the field of mass spectrometry have supported the use of metabolomics analysis for precision medicine. This growth has been allowed also by the application of algorithms to data analysis, including multivariate and machine learning methods, which are fundamental to managing large number of variables and samples. In the present review, we reported and discussed the application of artificial intelligence (AI) strategies for metabolomics data analysis. Particularly, we focused on widely used non-linear machine learning classifiers, such as ANN, random forest, and support vector machine (SVM) algorithms. A discussion of recent studies and research focused on disease classification, biomarker identification and early diagnosis is presented. Challenges in the implementation of metabolomics-AI systems, limitations thereof and recent tools were also discussed.

Importance of Surface Topography in Both Biological Activity and Catalysis of Nanomaterials: Can Catalysis by Design Guide Safe by Design?

It is acknowledged that the physicochemical properties of nanomaterials (NMs) have an impact on their toxicity and, eventually, their pathogenicity. These properties may include the NMs' surface chemical composition, size, shape, surface charge, surface area, and surface coating with ligands (which can carry different functional groups as well as proteins). Nanotopography, defined as the specific surface features at the nanoscopic scale, is not widely acknowledged as an important physicochemical property. It is known that the size and shape of NMs determine their nanotopography which, in turn, determines their surface area and their active sites. Nanotopography may also influence the extent of dissolution of NMs and their ability to adsorb atoms and molecules such as proteins. Consequently, the surface atoms (due to their nanotopography) can influence the orientation of proteins as well as their denaturation. However, although it is of great importance, the role of surface topography (nanotopography) in nanotoxicity is not much considered. Many of the issues that relate to nanotopography have much in common with the fundamental principles underlying classic catalysis. Although these were developed over many decades, there have been recent important and remarkable improvements in the development and study of catalysts. These have been brought about by new techniques that have allowed for study at the nanoscopic scale. Furthermore, the issue of quantum confinement by nanosized particles is now seen as an important issue in studying nanoparticles (NPs). In catalysis, the manipulation of a surface to create active surface sites that enhance interactions with external molecules and atoms has much in common with the interaction of NP surfaces with proteins, viruses, and bacteria with the same active surface sites of NMs. By reviewing the role that surface nanotopography plays in defining many of the NMs' surface properties, it reveals the need for its consideration as an important physicochemical property in descriptive and predictive toxicology. Through the manipulation of surface topography, and by using principles developed in catalysis, it may also be possible to make safe-by-design NMs with a reduction of the surface properties which contribute to their toxicity.

In Silico Identification and Evaluation of Natural Products as Potential Tumor Necrosis Factor Function Inhibitors Using Advanced Enalos Asclepios KNIME Nodes.

Tumor necrosis factor (TNF) is a regulator of several chronic inflammatory diseases, such as rheumatoid arthritis. Although anti-TNF biologics have been used in clinic, they render several drawbacks, such as patients' progressive immunodeficiency and loss of response, high cost, and intravenous administration. In order to find new potential anti-TNF small molecule inhibitors, we employed an in silico approach, aiming to find natural products, analogs of Ampelopsin H, a compound that blocks the formation of TNF active trimer. Two out of nine commercially available compounds tested, Nepalensinol B and Miyabenol A, efficiently reduced TNF-induced cytotoxicity in L929 cells and production of chemokines in mice joints' synovial fibroblasts, while Nepalensinol B also abolished TNF-TNFR1 binding in non-toxic concentrations. The binding mode of the compounds was further investigated by molecular dynamics and free energy calculation studies, using and advancing the Enalos Asclepios pipeline. Conclusively, we propose that Nepalensinol B, characterized by the lowest free energy of binding and by a higher number of hydrogen bonds with TNF, qualifies as a potential lead compound for TNF inhibitors' drug development. Finally, the upgraded Enalos Asclepios pipeline can be used for improved identification of new therapeutics against TNF-mediated chronic inflammatory diseases, providing state-of-the-art insight on their binding mode.

Advances in de Novo Drug Design: From Conventional to Machine Learning Methods.

. De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including machine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures. This method has successfully been employed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencoders. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and highlights hot topics for further development.

Zeta-Potential Read-Across Model Utilizing Nanodescriptors Extracted via the NanoXtract Image Analysis Tool Available on the Enalos Nanoinformatics Cloud Platform.

Zeta potential is one of the most critical properties of nanomaterials (NMs) which provides an estimation of the surface charge, and therefore electrostatic stability in medium and, in practical terms, influences the NM's tendency to form agglomerates and to interact with cellular membranes. This paper describes a robust and accurate read-across model to predict NM zeta potential utilizing as the input data a set of image descriptors derived from transmission electron microscopy (TEM) images of the NMs. The image descriptors are calculated using NanoXtract (http://enaloscloud.novamechanics.com/EnalosWebApps/NanoXtract/), a unique online tool that generates 18 image descriptors from the TEM images, which can then be explored by modeling to identify those most predictive of NM behavior and biological effects. NM TEM images are used to develop a model for prediction of zeta potential based on grouping of the NMs according to their nearest neighbors. The model provides interesting insights regarding the most important similarity features between NMs-in addition to core composition the main elongation emerged, which links to key drivers of NM toxicity such as aspect ratio. Both the NanoXtract image analysis tool and the validated model for zeta potential (http://enaloscloud.novamechanics.com/EnalosWebApps/ZetaPotential/) are freely available online through the Enalos Nanoinformatics platform.

Development of Deep Learning Models for Predicting the Effects of Exposure to Engineered Nanomaterials on Daphnia magna.

This study presents the results of applying deep learning methodologies within the ecotoxicology field, with the objective of training predictive models that can support hazard assessment and eventually the design of safer engineered nanomaterials (ENMs). A workflow applying two different deep learning architectures on microscopic images of Daphnia magna is proposed that can automatically detect possible malformations, such as effects on the length of the tail, and the overall size, and uncommon lipid concentrations and lipid deposit shapes, which are due to direct or parental exposure to ENMs. Next, classification models assign specific objects (heart, abdomen/claw) to classes that depend on lipid densities and compare the results with controls. The models are statistically validated in terms of their prediction accuracy on external D. magna images and illustrate that deep learning technologies can be useful in the nanoinformatics field, because they can automate time-consuming manual procedures, accelerate the investigation of adverse effects of ENMs, and facilitate the process of designing safer nanostructures. It may even be possible in the future to predict impacts on subsequent generations from images of parental exposure, reducing the time and cost involved in long-term reproductive toxicity assays over multiple generations.

Risk Governance of Emerging Technologies Demonstrated in Terms of its Applicability to Nanomaterials.

Nanotechnologies have reached maturity and market penetration that require nano-specific changes in legislation and harmonization among legislation domains, such as the amendments to REACH for nanomaterials (NMs) which came into force in 2020. Thus, an assessment of the components and regulatory boundaries of NMs risk governance is timely, alongside related methods and tools, as part of the global efforts to optimise nanosafety and integrate it into product design processes, via Safe(r)-by-Design (SbD) concepts. This paper provides an overview of the state-of-the-art regarding risk governance of NMs and lays out the theoretical basis for the development and implementation of an effective, trustworthy and transparent risk governance framework for NMs. The proposed framework enables continuous integration of the evolving state of the science, leverages best practice from contiguous disciplines and facilitates responsive re-thinking of nanosafety governance to meet future needs. To achieve and operationalise such framework, a science-based Risk Governance Council (RGC) for NMs is being developed. The framework will provide a toolkit for independent NMs' risk governance and integrates needs and views of stakeholders. An extension of this framework to relevant advanced materials and emerging technologies is also envisaged, in view of future foundations of risk research in Europe and globally.

Synthesis of novel 2-pyrrolidinone and pyrrolidine derivatives and study of their inhibitory activity against autotaxin enzyme.

Autotaxin (ATX), a glycoprotein (~125 kDa) isolated as an autocrine motility factor from melanoma cells, belongs to a seven-membered family of ectonucleotide pyrophosphatase/phosphodiesterase (ENPP), and exhibits lysophospholipase D activity. ATX is responsible for the hydrolysis of lysophosphatidylcholine (LPC) to produce the bioactive lipid lysophosphatidic acid (LPA), which is upregulated in a variety of pathological inflammatory conditions, including fibrosis, cancer, liver toxicity and thrombosis. Given its role in human disease, the ATX-LPA axis is an interesting target for therapy, and the development of novel potent ATX inhibitors is of great importance. In the present work a novel class of ATX inhibitors, optically active derivatives of 2-pyrrolidinone and pyrrolidine heterocycles were synthesized. Some of them exhibited interesting in vitro activity, namely the hydroxamic acid 16 (IC50 700 nM) and the carboxylic acid 40b (IC50 800 nM), while the boronic acid derivatives 3k (IC50 50 nM), 3l (IC50 120 nM), 3 m (IC50 180 nM) and 21 (IC50 35 nM) were found to be potent inhibitors of ATX.

Computer-Aided Drug Design of ß-Secretase, γ-Secretase and Anti-Tau Inhibitors for the Discovery of Novel Alzheimer's Therapeutics.

Aging-associated neurodegenerative diseases, which are characterized by progressive neuronal death and synapses loss in human brain, are rapidly growing affecting millions of people globally. Alzheimer's is the most common neurodegenerative disease and it can be caused by genetic and environmental risk factors. This review describes the amyloid-ß and Tau hypotheses leading to amyloid plaques and neurofibrillary tangles, respectively which are the predominant pathways for the development of anti-Alzheimer's small molecule inhibitors. The function and structure of the druggable targets of these two pathways including ß-secretase, γ-secretase, and Tau are discussed in this review article. Computer-Aided Drug Design including computational structure-based design and ligand-based design have been employed successfully to develop inhibitors for biomolecular targets involved in Alzheimer's. The application of computational molecular modeling for the discovery of small molecule inhibitors and modulators for ß-secretase and γ-secretase is summarized. Examples of computational approaches employed for the development of anti-amyloid aggregation and anti-Tau phosphorylation, proteolysis and aggregation inhibitors are also reported.

Structure-Based Discovery of Novel Chemical Classes of Autotaxin Inhibitors.

Autotaxin (ATX) is a secreted glycoprotein, widely present in biological fluids, largely responsible for extracellular lysophosphatidic acid (LPA) production. LPA is a bioactive growth-factor-like lysophospholipid that exerts pleiotropic effects in almost all cell types, exerted through at least six G-protein-coupled receptors (LPAR1-6). Increased ATX expression has been detected in different chronic inflammatory diseases, while genetic or pharmacological studies have established ATX as a promising therapeutic target, exemplified by the ongoing phase III clinical trial for idiopathic pulmonary fibrosis. In this report, we employed an in silico drug discovery workflow, aiming at the identification of structurally novel series of ATX inhibitors that would be amenable to further optimization. Towards this end, a virtual screening protocol was applied involving the search into molecular databases for new small molecules potentially binding to ATX. The crystal structure of ATX in complex with a known inhibitor (HA-155) was used as a molecular model docking reference, yielding a priority list of 30 small molecule ATX inhibitors, validated by a well-established enzymatic assay of ATX activity. The two most potent, novel and structurally different compounds were further structurally optimized by deploying further in silico tools, resulting to the overall identification of six new ATX inhibitors that belong to distinct chemical classes than existing inhibitors, expanding the arsenal of chemical scaffolds and allowing further rational design.

Development and therapeutic potential of autotaxin small molecule inhibitors: From bench to advanced clinical trials.

Several years after its isolation from melanoma cells, an increasing body of experimental evidence has established the involvement of Autotaxin (ATX) in the pathogenesis of several diseases. ATX, an extracellular enzyme responsible for the hydrolysis of lysophosphatidylcholine (LPC) into the bioactive lipid lysophosphatidic acid (LPA), is overexpressed in a variety of human metastatic cancers and is strongly implicated in chronic inflammation and liver toxicity, fibrotic diseases, and thrombosis. Accordingly, the ATX-LPA signaling pathway is considered a tractable target for therapeutic intervention substantiated by the multitude of research campaigns that have been successful in identifying ATX inhibitors by both academia and industry. Furthermore, from a therapeutic standpoint, the entry and the so far promising results of the first ATX inhibitor in advanced clinical trials against idiopathic pulmonary fibrosis (IPF) lends support to the viability of this approach, bringing it to the forefront of drug discovery efforts. The present review article aims to provide a comprehensive overview of the most important series of ATX inhibitors developed so far. Special weight is lent to the design, structure activity relationship and mode of binding studies carried out, leading to the identification of advanced leads. The most significant in vitro and in vivo pharmacological results of these advanced leads are also summarized. Lastly, the development of the first ATX inhibitor entered in clinical trials accompanied by its phase 1 and 2a clinical trial data is disclosed.

Cheminformatics-aided discovery of small-molecule Protein-Protein Interaction (PPI) dual inhibitors of Tumor Necrosis Factor (TNF) and Receptor Activator of NF-κB Ligand (RANKL).

We present an in silico drug discovery pipeline developed and applied for the identification and virtual screening of small-molecule Protein-Protein Interaction (PPI) compounds that act as dual inhibitors of TNF and RANKL through the trimerization interface. The cheminformatics part of the pipeline was developed by combining structure-based with ligand-based modeling using the largest available set of known TNF inhibitors in the literature (2481 small molecules). To facilitate virtual screening, the consensus predictive model was made freely available at: http://enalos.insilicotox.com/TNFPubChem/. We thus generated a priority list of nine small molecules as candidates for direct TNF function inhibition. In vitro evaluation of these compounds led to the selection of two small molecules that act as potent direct inhibitors of TNF function, with IC50 values comparable to those of a previously-described direct inhibitor (SPD304), but with significantly reduced toxicity. These molecules were also identified as RANKL inhibitors and validated in vitro with respect to this second functionality. Direct binding of the two compounds was confirmed both for TNF and RANKL, as well as their ability to inhibit the biologically-active trimer forms. Molecular dynamics calculations were also carried out for the two small molecules in each protein to offer additional insight into the interactions that govern TNF and RANKL complex formation. To our knowledge, these compounds, namely T8 and T23, constitute the second and third published examples of dual small-molecule direct function inhibitors of TNF and RANKL, and could serve as lead compounds for the development of novel treatments for inflammatory and autoimmune diseases.

Exploiting ChEMBL database to identify indole analogs as HCV replication inhibitors.

Molecular docking, 3D-QSAR CoMSIA and similarity search were combined in a multi-step framework with the ultimate goal to identify potent indole analogs, in the ChEMBL database, as inhibitors of HCV replication. The crystal structure of HCV RNA-dependent RNA polymerase (NS5B GT1b) was utilized and 41 known inhibitors were docked into the enzyme "Palm II" active site. In a second step, the docking pose of each compound was used in a receptor-based alignment for the generation of the CoMSIA fields. A validated 3D-QSAR CoMSIA model was subsequently built to accurately estimate the activity values. The proposed framework gives insight into the structural characteristics that affect the binding and the inhibitory activity of these analogs on HCV polymerase. The obtained in silico model was used to predict the activity of novel compounds prior to their synthesis and biological testing, within a Virtual Screening framework. The ChEMBL database was mined to afford compounds containing the indole scaffold that are predicted to possess high activity and thus can be prioritized for biological screening.