Best Practices of Computer-Aided Drug Discovery: Lessons Learned from the Development of a Preclinical Candidate for Prostate Cancer with a New Mechanism of Action

HydraScreen: A Generalizable Structure-Based Deep Learning Approach to Drug Discovery

We propose HydraScreen, a deep-learning framework for safe and robust accelerated drug discovery. HydraScreen utilizes a state-of-the-art 3D convolutional neural network designed for the effective representation of molecular structures and interactions in protein-ligand binding. We designed an end-to-end pipeline for high-throughput screening and lead optimization, targeting applications in structure-based drug design. We assessed our approach using established public benchmarks based on the CASF-2016 core set, achieving top-tier results in affinity and pose prediction (Pearson's r = 0.86, RMSE = 1.15, Top-1 = 0.95). We introduced a novel approach for interaction profiling, aimed at detecting potential biases within both the model and data sets. This approach not only enhanced interpretability but also reinforced the impartiality of our methodology. Finally, we demonstrated HydraScreen's ability to generalize effectively across novel proteins and ligands through a temporal split. We also provide insights into potential avenues for future development aimed at enhancing the robustness of machine learning scoring functions. HydraScreen (accessible at http://hydrascreen.ro5.ai/paper) provides a user-friendly GUI and a public API, facilitating the easy-access assessment of protein-ligand complexes.

Exploring the evolutionary landscape and structural resonances of ferritin with insights into functional significance in plant

The mineral iron plays a crucial role in facilitating the optimal functioning of numerous biological processes within the cellular environment. These processes involve the transportation of oxygen, energy production, immune system functioning, cognitive abilities, and muscle function. However, it is crucial to note that excessive levels of iron can result in oxidative damage within cells, primarily through Fenton reactions. Iron availability and toxicity present significant challenges that have been addressed through evolution. Ferritin is an essential protein that stores iron and is divided into different subfamilies, including DNA-binding proteins under starvation (Dps), bacterioferritin, and classical ferritin. Ferritin plays a critical role in maintaining cellular balance and protecting against oxidative damage. This study delves into ferritin's evolutionary dynamics across diverse taxa, emphasizing structural features and regulatory mechanisms. Insights into ferritin's evolution and functional diversity are gained through phylogenetic and structural analysis in bacterial Dps, bacterioferritin, and classical ferritin proteins. Additionally, the involvement of ferritin in plant stress responses and development is explored. Analysis of ferritin gene expression across various developmental stages and stress conditions provides insights into its regulatory roles. This comprehensive exploration enhances our understanding of ferritin's significance in plant biology, offering insights into its evolutionary history, structural diversity, and protective mechanisms against oxidative stress.

Developing phytocompound-based new drugs against multi-drug-resistant Staphylococcus aureus

Staphylococcus aureus, a prevalent component of the human microbiota, is associated with skin infections to life-threatening diseases, presenting challenges in treatment options and necessitating the development of effective treatments. This study integrated computational and in vitro approaches to identify promising phytocompounds with therapeutic potential. Staphopain B emerged as a target protein for its role in immune evasion, exhibiting stability during molecular dynamic simulation (MDS) with a root mean square deviation value of 2.376 Å. Screening 115 phytocompounds with antibacterial properties from the PubChem database identified 12 with drug-like properties, nine of which showed superior binding affinity to Staphopain B compared to a commercial antibiotic, doxycycline (-7.8 kcal mol-1). Notably, epoxyazadiradione and nimbolide displayed higher estimated free energy of binding scores (-7.91 and -7.93 kcal mol-1, respectively), indicating strong protein-ligand interactions. The root mean square fluctuation values for epoxyazadiradione and nimbolide were 1.097 and 1.034 Å, respectively, which was confirmed through MDS. Crude ethanolic extracts (100% and 70%) of neem (Azadirachta indica) leaves demonstrated narrow inhibition against the bacteria in comparison to doxycycline in the disc-diffusion assay. This study underscores the potential of phytocompounds as therapeutic agents against S. aureus; however, further in vitro experiments and testing of the phytocompounds in vivo are required.

Recent advances in dietary androgen receptor inhibitors

As a nuclear transcription factor, the androgen receptor (AR) plays a crucial role not only in normal male sexual differentiation and growth of the prostate, but also in benign prostatic hyperplasia, prostatitis, and prostate cancer. Multiple population-based epidemiological studies demonstrated that prostate cancer risk was inversely associated with increased dietary intakes of green tea, soy products, tomato, and so forth. Therefore, this review aimed to summarize the structure and function of AR, and further illustrate the structural basis for antagonistic mechanisms of the currently clinically available antiandrogens. Due to the limitations of these antiandrogens, a series of natural AR inhibitors have been identified from edible plants such as fruits and vegetables, as well as folk medicines, health foods, and nutritional supplements. Hence, this review mainly focused on recent experimental, epidemiological, and clinical studies about natural AR inhibitors, particularly the association between dietary intake of natural antiandrogens and reduced risk of prostatic diseases. Since natural products offer multiple advantages over synthetic antiandrogens, this review may provide a comprehensive and updated overview of dietary-derived AR inhibitors, as well as their potential for the nutritional intervention against prostatic disorders.

In silico screening of LRRK2 WDR domain inhibitors using deep docking and free energy simulations

The Critical Assessment of Computational Hit-Finding Experiments (CACHE) Challenge series is focused on identifying small molecule inhibitors of protein targets using computational methods. Each challenge contains two phases, hit-finding and follow-up optimization, each of which is followed by experimental validation of the computational predictions. For the CACHE Challenge #1, the Leucine-Rich Repeat Kinase 2 (LRRK2) WD40 Repeat (WDR) domain was selected as the target for in silico hit-finding and optimization. Mutations in LRRK2 are the most common genetic cause of the familial form of Parkinson's disease. The LRRK2 WDR domain is an understudied drug target with no known molecular inhibitors. Herein we detail the first phase of our winning submission to the CACHE Challenge #1. We developed a framework for the high-throughput structure-based virtual screening of a chemically diverse small molecule space. Hit identification was performed using the large-scale Deep Docking (DD) protocol followed by absolute binding free energy (ABFE) simulations. ABFEs were computed using an automated molecular dynamics (MD)-based thermodynamic integration (TI) approach. 4.1 billion ligands from Enamine REAL were screened with DD followed by ABFEs computed by MD TI for 793 ligands. 76 ligands were prioritized for experimental validation, with 59 compounds successfully synthesized and 5 compounds identified as hits, yielding a 8.5% hit rate. Our results demonstrate the efficacy of the combined DD and ABFE approaches for hit identification for a target with no previously known hits. This approach is widely applicable for the efficient screening of ultra-large chemical libraries as well as rigorous protein-ligand binding affinity estimation leveraging modern computational resources.

An interdisciplinary course on computer-aided drug discovery to broaden student participation in original scientific research

We present a new highly interdisciplinary project-based course in computer aided drug discovery (CADD). This course was developed in response to a call for alternative pedagogical approaches during the COVID-19 pandemic, which caused the cancellation of a face-to-face summer research program sponsored by the Louisiana Biomedical Research Network (LBRN). The course integrates guided research and educational experiences for chemistry, biology, and computer science students. We implement research-based methods with publicly available tools in bioinformatics and molecular modeling to identify and prioritize promising antiviral drug candidates for COVID-19. The purpose of this course is three-fold: I. Implement an active learning and inclusive pedagogy that fosters student engagement and research mindset; II. Develop student interdisciplinary research skills that are highly beneficial in a broader scientific context; III. Demonstrate that pedagogical shifts (initially incurred during the COVID-19 pandemic) can furnish longer-term instructional benefits. The course, which has now been successfully taught a total of five times, incorporates four modules, including lectures/discussions, live demos, inquiry-based assignments, and science communication.

PopShift: A Thermodynamically Sound Approach to Estimate Binding Free Energies by Accounting for Ligand-Induced Population Shifts from a Ligand-Free Markov State Model

Obtaining accurate binding free energies from in silico screens has been a long-standing goal for the computational chemistry community. However, accuracy and computational cost are at odds with one another, limiting the utility of methods that perform this type of calculation. Many methods achieve massive scale by explicitly or implicitly assuming that the target protein adopts a single structure, or undergoes limited fluctuations around that structure, to minimize computational cost. Others simulate each protein-ligand complex of interest, accepting lower throughput in exchange for better predictions of binding affinities. Here, we present the PopShift framework for accounting for the ensemble of structures a protein adopts and their relative probabilities. Protein degrees of freedom are enumerated once, and then arbitrarily many molecules can be screened against this ensemble. Specifically, we use Markov state models (MSMs) as a compressed representation of a protein's thermodynamic ensemble. We start with a ligand-free MSM and then calculate how addition of a ligand shifts the populations of each protein conformational state based on the strength of the interaction between that protein conformation and the ligand. In this work we use docking to estimate the affinity between a given protein structure and ligand, but any estimator of binding affinities could be used in the PopShift framework. We test PopShift on the classic benchmark pocket T4 Lysozyme L99A. We find that PopShift is more accurate than common strategies, such as docking to a single structure and traditional ensemble docking─producing results that compare favorably with alchemical binding free energy calculations in terms of RMSE but not correlation─and may have a more favorable computational cost profile in some applications. In addition to predicting binding free energies and ligand poses, PopShift also provides insight into how the probability of different protein structures is shifted upon addition of various concentrations of ligand, providing a platform for predicting affinities and allosteric effects of ligand binding. Therefore, we expect PopShift will be valuable for hit finding and for providing insight into phenomena like allostery.

Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR

Quantitative structure-activity relationship (QSAR) modelling, an approach that was introduced 60 years ago, is widely used in computer-aided drug design. In recent years, progress in artificial intelligence techniques, such as deep learning, the rapid growth of databases of molecules for virtual screening and dramatic improvements in computational power have supported the emergence of a new field of QSAR applications that we term 'deep QSAR'. Marking a decade from the pioneering applications of deep QSAR to tasks involved in small-molecule drug discovery, we herein describe key advances in the field, including deep generative and reinforcement learning approaches in molecular design, deep learning models for synthetic planning and the application of deep QSAR models in structure-based virtual screening. We also reflect on the emergence of quantum computing, which promises to further accelerate deep QSAR applications and the need for open-source and democratized resources to support computer-aided drug design.

Artificial intelligence for prediction of biological activities and generation of molecular hits using stereochemical information

In this work, we develop a method for generating targeted hit compounds by applying deep reinforcement learning and attention mechanisms to predict binding affinity against a biological target while considering stereochemical information. The novelty of this work is a deep model Predictor that can establish the relationship between chemical structures and their corresponding [Formula: see text] values. We thoroughly study the effect of different molecular descriptors such as ECFP4, ECFP6, SMILES and RDKFingerprint. Also, we demonstrated the importance of attention mechanisms to capture long-range dependencies in molecular sequences. Due to the importance of stereochemical information for the binding mechanism, this information was employed both in the prediction and generation processes. To identify the most promising hits, we apply the self-adaptive multi-objective optimization strategy. Moreover, to ensure the existence of stereochemical information, we consider all the possible enumerated stereoisomers to provide the most appropriate 3D structures. We evaluated this approach against the Ubiquitin-Specific Protease 7 (USP7) by generating putative inhibitors for this target. The predictor with SMILES notations as descriptor plus bidirectional recurrent neural network using attention mechanism has the best performance. Additionally, our methodology identify the regions of the generated molecules that are important for the interaction with the receptor's active site. Also, the obtained results demonstrate that it is possible to discover synthesizable molecules with high biological affinity for the target, containing the indication of their optimal stereochemical conformation.

Silibinin: an inhibitor for a high-expressed BCL-2A1/BFL1 protein, linked with poor prognosis in breast cancer

Breast cancer (BC) accounts for 30% of all diagnosed cases of cancer in women and remains a leading cause of cancer-related deaths among women worldwide. The current study looks for a protein from the anti-apoptotic/pro-survival BCL-2 family whose overexpression reduces survivability in BC patients and a potential inhibitor for the protein. We found BCL-2A1/BFL1 protein with high expression linked to low survivability in BC. The protein shows prognosis in 8 out of 29 categories, whereas no other family member manifests this property. Out of 7379 compounds, three small molecules (CHEMBL9509, CHEMBL2104550 and CHEMBL3545011) form an H-bond with BCL-2A1/BFL1 protein's unique residue Cys55. Of the three small molecules, we found CHEMBL9509 (Silibinin) to be a potent inhibitor. The compound forms a stable H-bond with the residue Cys55 with the lowest binding energy compared to the other two compounds. It remains stable in the BH3 binding region for more than 100 ns, whereas the other two detach from the region. Additionally, the compound is found to be better than Venetoclax and Nematoclax. We firmly believe in the compound CHEMBL9509 potency to halt BC's progression by inhibiting the BCL-2A1/BFL1 protein, increasing patients' survivability.Communicated by Ramaswamy H. Sarma.

Identification and validation of a small molecule targeting ROR1 for the treatment of triple negative breast cancer

Introduction: Breast cancer is the most common cancer in women, with roughly 10-15% of new cases classified as triple-negative breast cancer (TNBC). Traditional chemotherapies are often toxic to normal cells. Therefore, it is important to discover new anticancer compounds that target TNBC while causing minimal damage to normal cells. Receptor tyrosine kinase-like Orphan Receptor 1 (ROR1) is an oncofetal protein overexpressed in numerous human malignancies, including TNBC. This study investigated potential small molecules targeting ROR1. Methodology: Using AutoDock Vina and Glide, we screened 70,000 chemicals for our investigation. We obtained 10 representative compounds via consensus voting, deleting structural alerts, and clustering. After manual assessment, compounds 2 and 4 were chosen for MD simulation and cell viability experiment. Compound 4 showed promising results in the viability assay, which led us to move further with the apoptosis assay and immunoblotting. Results: Compound 4 (CID1261330) had docking scores of -6.635 and -10.8. It fits into the pocket and shows interactions with GLU64, ASP174, and PHE93. Its RMSD fluctuates around 0.20 nm and forms two stable H-bonds indicating compound 4 stability. It inhibits cell proliferation in MDA-MB-231, HCC1937, and HCC1395 cell lines, with IC50 values of approximately 2 μM to 10 μM, respectively. Compound 4 did not kill non-malignant epithelial breast cells MCF-10A (IC50 > 27 μM). These results were confirmed by the significant number of apoptotic cells in MDA-MB-231 cells (47.6%) but not in MCF-10A cells (7.3%). Immunoblot analysis provided additional support in the same direction. Discussion: These findings collectively suggest that compound 4 has the potential to effectively eliminate TNBC cells while causing minimal harm to normal breast cells. The promising outcomes of this study lay the groundwork for further testing of compound 4 in other malignancies characterized by ROR1 upregulation, serving as a proof-of-concept for its broader applicability.

Current and emerging approaches to noncompetitive AR inhibition

The androgen receptor (AR) has been shown to be a key determinant in the pathogenesis of castration-resistant prostate cancer (CRPC). The current standard of care therapies targets the ligand-binding domain of the receptor and can afford improvements to life expectancy often only in the order of months before resistance occurs. Emerging preclinical and clinical compounds that inhibit receptor activity via differentiated mechanisms of action which are orthogonal to current antiandrogens show promise for overcoming treatment resistance. In this review, we present an authoritative summary of molecules that noncompetitively target the AR. Emerging small molecule strategies for targeting alternative domains of the AR represent a promising area of research that shows significant potential for future therapies. The overall quality of lead candidates in the area of noncompetitive AR inhibition is discussed, and it identifies the key chemotypes and associated properties which are likely to be, or are currently, positioned to be first in human applications.

PopShift: A thermodynamically sound approach to estimate binding free energies by accounting for ligand-induced population shifts from a ligand-free MSM

Obtaining accurate binding free energies from in silico screens has been a longstanding goal for the computational chemistry community. However, accuracy and computational cost are at odds with one another, limiting the utility of methods that perform this type of calculation. Many methods achieve massive scale by explicitly or implicitly assuming that the target protein adopts a single structure, or undergoes limited fluctuations around that structure, to minimize computational cost. Others simulate each protein-ligand complex of interest, accepting lower throughput in exchange for better predictions of binding affinities. Here, we present the PopShift framework for accounting for the ensemble of structures a protein adopts and their relative probabilities. Protein degrees of freedom are enumerated once, and then arbitrarily many molecules can be screened against this ensemble. Specifically, we use Markov state models (MSMs) as a compressed representation of a protein's thermodynamic ensemble. We start with a ligand-free MSM and then calculate how addition of a ligand shifts the populations of each protein conformational state based on the strength of the interaction between that protein conformation and the ligand. In this work we use docking to estimate the affinity between a given protein structure and ligand, but any estimator of binding affinities could be used in the PopShift framework. We test PopShift on the classic benchmark pocket T4 Lysozyme L99A. We find that PopShift is more accurate than common strategies, such as docking to a single structure and traditional ensemble docking-producing results that compare favorably with alchemical binding free energy calculations in terms of RMSE but not correlation - and may have a more favorable computational cost profile in some applications. In addition to predicting binding free energies and ligand poses, PopShift also provides insight into how the probability of different protein structures is shifted upon addition of various concentrations of ligand, providing a platform for predicting affinities and allosteric effects of ligand binding. Therefore, we expect PopShift will be valuable for hit finding and for providing insight into phenomena like allostery.

Novel Inhibitors of androgen receptor's DNA binding domain identified using an ultra-large virtual screening

Androgen receptor (AR) inhibition remains the primary strategy to combat the progression of prostate cancer (PC). However, all clinically used AR inhibitors target the ligand-binding domain (LBD), which is highly susceptible to truncations through splicing or mutations that confer drug resistance. Thus, there exists an urgent need for AR inhibitors with novel modes of action. We thus launched a virtual screening of an ultra-large chemical library to find novel inhibitors of the AR DNA-binding domain (DBD) at two sites: protein-DNA interface (P-box) and dimerization site (D-box). The compounds selected through vigorous computational filtering were then experimentally validated. We identified several novel chemotypes that effectively suppress transcriptional activity of AR and its splice variant V7. The identified compounds represent previously unexplored chemical scaffolds with a mechanism of action that evades the conventional drug resistance manifested through LBD mutations. Additionally, we describe the binding features required to inhibit AR DBD at both P-box and D-box target sites.

Automated machine learning approach for developing a quantitative structure-activity relationship model for cardiac steroid inhibition of Na+/K+-ATPase

BACKGROUND

Quantitative structure-activity relationship (QSAR) modeling is a method of characterizing the relationship between chemical structures and biological activity. Automated machine learning enables computers to learn from large datasets and can be used for chemoinformatics. Cardiac steroids (CSs) inhibit the activity of Na⁺/K⁺-ATPase (NKA) in several species, including humans, since the binding pocket in which NKA binds to CSs is highly conserved. CSs are used to treat heart disease and have been developed into anticancer drugs for use in clinical trials. Novel CSs are, therefore, frequently synthesized and their activities evaluated. The purpose of this study is to develop a QSAR model via automated machine learning to predict the potential inhibitory activity of compounds without performing experiments.

METHODS

The chemical structures and inhibitory activities of 215 CS derivatives were obtained from the scientific literature. Predictive QSAR models were constructed using molecular descriptors, fingerprints, and biological activities.

RESULTS

The best predictive QSAR models were selected based on the LogLoss value. Using these models, the Matthews correlation coefficient, F1 score, and area under the curve of the test dataset were 0.6729, 0.8813, and 0.8812, respectively. Next, we showed automated construction of the predictive models for CS derivatives, which may be useful for identifying novel CSs suitable for candidate drug development.

CONCLUSION

The automated machine learning-based QSAR method developed here should be applicable for the time-efficient construction of predictive models using only a small number of compounds.

Preparation and characterization of amoxapine- and naringin-loaded solid lipid nanoparticles: drug-release and molecular-docking studies

Aim: Amoxapine (AMX) has been reported to be metabolized by CYP3A4 and CYP2D6. Naringin (NG) has been reported to inhibit CYP enzymes. Therefore, the current work was designed to develop AMX solid lipid nanoparticles (AMX-SLNs) and NG-SLNs for better therapeutic performance. Materials & methods: AMX-SLNs and NG-SLNs were prepared and characterized. AMX and NG interactions with CYP450s were studied with molecular docking to rationalize the effectiveness of the combination. Results: AMX-SLNs and NG-SLNs showed nanometric size with a sustained in vitro drug-release profile. NG showed a higher predicted binding affinity for CYP3A4 and CYP2D6, suggesting the potential for inhibition. Conclusion: The developed formulations were thoroughly characterized along with molecular docking data indicating promising AMX and NG combinations that may show good therapeutic activity.

Surely you are joking, Mr Docking!

In the wake of recent COVID-19 pandemics scientists around the world rushed to deliver numerous CADD (Computer-Aided Drug Discovery) methods and tools that could be reliably used to discover novel drug candidates against the SARS-CoV-2 virus. With that, there emerged a trend of a significant democratization of CADD that contributed to the rapid development of various COVID-19 drug candidates currently undergoing different stages of validation. On the other hand, this democratization also inadvertently led to the surge rapidly performed molecular docking studies to nominate multiple scores of novel drug candidates supported by computational arguments only. Albeit driven by best intentions, most of such studies also did not follow best practices in the field that require experience and expertise learned through multiple rigorously designed benchmarking studies and rigorous experimental validation. In this Viewpoint we reflect on recent disbalance between small number of rigorous and comprehensive studies and the proliferation of purely computational studies enabled by the ease of docking software availability. We further elaborate on the hyped oversale of CADD methods' ability to rapidly yield viable drug candidates and reiterate the critical importance of rigor and adherence to the best practices of CADD in view of recent emergence of AI and Big Data in the field.

Cytotoxic Potential of Bioactive Compounds from Aspergillus flavus, an Endophytic Fungus Isolated from Cynodon dactylon, against Breast Cancer: Experimental and Computational Approach

Endophytic fungi are a diverse group of microorganisms that colonize the inter- or intracellular spaces of plants and exhibit mutual benefits. Their interactions with the host plant and other microbiomes are multidimensional and play a crucial role in the production of secondary metabolites. We screened bioactive compounds present in the extracts of Aspergillus flavus, an endophytic fungus isolated from the roots of the medicinal grass Cynodon dactylon, for its anticancer potential. An in vitro analysis of the Ethyl acetate extract from A. flavus showed significant cytostatic effects (IC₅₀: 16.25 μg/mL) against breast cancer cells (MCF-7). A morphological analysis of the cells and a flow cytometry of the cells with annexin V/Propidium Iodide suggested that the extract induced apoptosis in the MCF-7 cells. The extract of A. flavus increased reactive oxygen species (ROS) generation and caused a loss of mitochondrial membrane potential in MCF-7 cells. To identify the metabolites that might be responsible for the anticancer effect, the extract was subjected to a gas chromatography-mass spectrometry (GC-MS) analysis. Interestingly, nine phytochemicals that induced cytotoxicity in the breast cancer cell line were found in the extract. The in silico molecular docking and molecular dynamics simulation studies revealed that two compounds, 2,4,7-trinitrofluorenone and 3α, 5 α-cyclo-ergosta-7,9(11), 22t-triene-6beta-ol exhibited significant binding affinities (-9.20, and -9.50 Kcal/mol, respectively) against Bcl-2, along with binding stability and intermolecular interactions of its ligand-Bcl-2 complexes. Overall, the study found that the endophytic A. flavus from C. dactylon contains plant-like bioactive compounds that have a promising effect in breast cancer.

Druggable Metabolic Vulnerabilities Are Exposed and Masked during Progression to Castration Resistant Prostate Cancer

There is an urgent need for exploring new actionable targets other than androgen receptor to improve outcome from lethal castration-resistant prostate cancer. Tumor metabolism has reemerged as a hallmark of cancer that drives and supports oncogenesis. In this regard, it is important to understand the relationship between distinctive metabolic features, androgen receptor signaling, genetic drivers in prostate cancer, and the tumor microenvironment (symbiotic and competitive metabolic interactions) to identify metabolic vulnerabilities. We explore the links between metabolism and gene regulation, and thus the unique metabolic signatures that define the malignant phenotypes at given stages of prostate tumor progression. We also provide an overview of current metabolism-based pharmacological strategies to be developed or repurposed for metabolism-based therapeutics for castration-resistant prostate cancer.

Protein-Based Virtual Screening Tools Applied for RNA-Ligand Docking Identify New Binders of the preQ1-Riboswitch

Targeting RNA with small molecules is an emerging field. While several ligands for different RNA targets are reported, structure-based virtual screenings (VSs) against RNAs are still rare. Here, we elucidated the general capabilities of protein-based docking programs to reproduce native binding modes of small-molecule RNA ligands and to discriminate known binders from decoys by the scoring function. The programs were found to perform similar compared to the RNA-based docking tool rDOCK, and the challenges faced during docking, namely, protomer and tautomer selection, target dynamics, and explicit solvent, do not largely differ from challenges in conventional protein-ligand docking. A prospective VS with the Bacillus subtilis preQ₁-riboswitch aptamer domain performed with FRED, HYBRID, and FlexX followed by microscale thermophoresis assays identified six active compounds out of 23 tested VS hits with potencies between 29.5 nM and 11.0 μM. The hits were selected not solely based on their docking score but for resembling key interactions of the native ligand. Therefore, this study demonstrates the general feasibility to perform structure-based VSs against RNA targets, while at the same time it highlights pitfalls and their potential solutions when executing RNA-ligand docking.

Discovery of novel antagonists targeting the DNA binding domain of androgen receptor by integrated docking-based virtual screening and bioassays

Androgen receptor (AR), a ligand-activated transcription factor, is a master regulator in the development and progress of prostate cancer (PCa). A major challenge for the clinically used AR antagonists is the rapid emergence of resistance induced by the mutations at AR ligand binding domain (LBD), and therefore the discovery of novel anti-AR therapeutics that can combat mutation-induced resistance is quite demanding. Therein, blocking the interaction between AR and DNA represents an innovative strategy. However, the hits confirmed targeting on it so far are all structurally based on a sole chemical scaffold. In this study, an integrated docking-based virtual screening (VS) strategy based on the crystal structure of the DNA binding domain (DBD) of AR was conducted to search for novel AR antagonists with new scaffolds and 2-(2-butyl-1,3-dioxoisoindoline-5-carboxamido)-4,5-dimethoxybenzoicacid (Cpd39) was identified as a potential hit, which was competent to block the binding of AR DBD to DNA and showed decent potency against AR transcriptional activity. Furthermore, Cpd39 was safe and capable of effectively inhibiting the proliferation of PCa cell lines (i.e., LNCaP, PC3, DU145, and 22RV1) and reducing the expression of the genes regulated by not only the full-length AR but also the splice variant AR-V7. The novel AR DBD-ARE blocker Cpd39 could serve as a starting point for the development of new therapeutics for castration-resistant PCa.

Automated discovery of noncovalent inhibitors of SARS-CoV-2 main protease by consensus Deep Docking of 40 billion small molecules

Recent explosive growth of 'make-on-demand' chemical libraries brought unprecedented opportunities but also significant challenges to the field of computer-aided drug discovery. To address this expansion of the accessible chemical universe, molecular docking needs to accurately rank billions of chemical structures, calling for the development of automated hit-selecting protocols to minimize human intervention and error. Herein, we report the development of an artificial intelligence-driven virtual screening pipeline that utilizes Deep Docking with Autodock GPU, Glide SP, FRED, ICM and QuickVina2 programs to screen 40 billion molecules against SARS-CoV-2 main protease (Mpro). This campaign returned a significant number of experimentally confirmed inhibitors of Mpro enzyme, and also enabled to benchmark the performance of twenty-eight hit-selecting strategies of various degrees of stringency and automation. These findings provide new starting scaffolds for hit-to-lead optimization campaigns against Mpro and encourage the development of fully automated end-to-end drug discovery protocols integrating machine learning and human expertise.

Taxifolin Targets PI3K and mTOR and Inhibits Glioblastoma Multiforme

Glioblastoma multiforme (GBM), the most common malignant primary brain tumor, has a very poor prognosis. With increasing knowledge of tumor molecular biology, targeted therapies are becoming increasingly integral to comprehensive GBM treatment strategies. mTOR is a key downstream molecule of the PI3K/Akt signaling pathway, integrating input signals from growth factors, nutrients, and energy sources to regulate cell growth and cell proliferation through multiple cellular responses. mTOR/PI3K dual-targeted therapy has shown promise in managing various cancers. Here, we report that taxifolin, a flavanone commonly found in milk thistle, inhibited mTOR/PI3K, promoted autophagy, and suppressed lipid synthesis in GBM. In silico analysis showed that taxifolin can bind to the rapamycin binding site of mTOR and the catalytic site of PI3K (p110α). In in vitro experiments, taxifolin inhibited mTOR and PI3K activity in five different glioma cell lines. Lastly, we showed that taxifolin suppressed tumors in mice; stimulated expression of autophagy-related genes LC3B-II, Atg7, atg12, and Beclin-1; and inhibited expression of fatty acid synthesis-related genes C/EBPα, PPARγ, FABP4, and FAS. Our observations suggest that taxifolin is potentially a valuable drug for treating GBM.

PrepFlow: A Toolkit for Chemical Library Preparation and Management for Virtual Screening

In the era of big data in Chemistry, the need for automated tools for virtual screening is compelling. Here, we present PrepFlow a toolkit for chemical library preparation and management. Starting from a list of compounds in SMILES or 2D molecular format, PrepFlow outputs a set of 3D molecular structures ready for use in subsequent drug discovery projects. Our development stands out for speed and robustness of execution, the efficient exploitation of HPC resources, and the implementation of an archiving strategy to save computer time, storage, and human intervention. Using a random selection of 600 compounds from available drug banks, we show that the preparation time per ligand on a desktop computer is 6.6 s. Thanks to these performances and the automatic parallelization on HPC, a chemical library of the size of ChEMBL (2 M) was prepared in around 3 days on a computer cluster. PrepFlow is freely distributed at the following link: https://ifm.chimie.unistra.fr/prepflow.

A critical overview of computational approaches employed for COVID-19 drug discovery

COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the most severe disruptions to societies and economies since the Great Depression. Massive experimental and computational research effort to understand and characterize the disease and rapidly develop diagnostics, vaccines, and drugs has emerged in response to this devastating pandemic and more than 130 000 COVID-19-related research papers have been published in peer-reviewed journals or deposited in preprint servers. Much of the research effort has focused on the discovery of novel drug candidates or repurposing of existing drugs against COVID-19, and many such projects have been either exclusively computational or computer-aided experimental studies. Herein, we provide an expert overview of the key computational methods and their applications for the discovery of COVID-19 small-molecule therapeutics that have been reported in the research literature. We further outline that, after the first year the COVID-19 pandemic, it appears that drug repurposing has not produced rapid and global solutions. However, several known drugs have been used in the clinic to cure COVID-19 patients, and a few repurposed drugs continue to be considered in clinical trials, along with several novel clinical candidates. We posit that truly impactful computational tools must deliver actionable, experimentally testable hypotheses enabling the discovery of novel drugs and drug combinations, and that open science and rapid sharing of research results are critical to accelerate the development of novel, much needed therapeutics for COVID-19.

Design of gp120 HIV-1 entry inhibitors by scaffold hopping via isosteric replacements

We present the development of alternative scaffolds and validation of their synthetic pathways as a tool for the exploration of new HIV gp120 inhibitors based on the recently discovered inhibitor of this class, NBD-14136. The new synthetic routes were based on isosteric replacements of the amine and acid precursors required for the synthesis of NBD-14136, guided by molecular modeling and chemical feasibility analysis. To ensure that these synthetic tools and new scaffolds had the potential for further exploration, we eventually tested few representative compounds from each newly designed scaffold against the gp120 inhibition assay and cell viability assays.

Structure-based molecular modeling in SAR analysis and lead optimization

In silico methods like molecular docking and pharmacophore modeling are established strategies in lead identification. Their successful application for finding new active molecules for a target is reported by a plethora of studies. However, once a potential lead is identified, lead optimization, with the focus on improving potency, selectivity, or pharmacokinetic parameters of a parent compound, is a much more complex task. Even though in silico molecular modeling methods could contribute a lot of time and cost-saving by rationally filtering synthetic optimization options, they are employed less widely in this stage of research. In this review, we highlight studies that have successfully used computer-aided SAR analysis in lead optimization and want to showcase sound methodology and easily accessible in silico tools for this purpose.

Development of Novel Inhibitors Targeting the D-Box of the DNA Binding Domain of Androgen Receptor

The inhibition of the androgen receptor (AR) is an established strategy in prostate cancer (PCa) treatment until drug resistance develops either through mutations in the ligand-binding domain (LBD) portion of the receptor or its deletion. We previously identified a druggable pocket on the DNA binding domain (DBD) dimerization surface of the AR and reported several potent inhibitors that effectively disrupted DBD-DBD interactions and consequently demonstrated certain antineoplastic activity. Here we describe further development of small molecule inhibitors of AR DBD dimerization and provide their broad biological characterization. The developed compounds demonstrate improved activity in the mammalian two-hybrid assay, enhanced inhibition of AR-V7 transcriptional activity, and improved microsomal stability. These findings position us for the development of AR inhibitors with entirely novel mechanisms of action that would bypass most forms of PCa treatment resistance, including the truncation of the LBD of the AR.

Discovery of New Catalytic Topoisomerase II Inhibitors for Anticancer Therapeutics

Poison inhibitors of DNA topoisomerase II (TOP2) are clinically used drugs that cause cancer cell death by inducing DNA damage, which mechanism of action is also associated with serious side effects such as secondary malignancy and cardiotoxicity. In contrast, TOP2 catalytic inhibitors induce limited DNA damage, have low cytotoxicity, and are effective in suppressing cancer cell proliferation. They have been sought after to be prospective anticancer therapies. Herein the discovery of new TOP2 catalytic inhibitors is described. A new druggable pocket of TOP2 protein at its DNA binding domain was used as a docking site to virtually screen ~6 million molecules from the ZINC15 library. The lead compound, T60, was characterized to be a catalytic TOP2 inhibitor that binds TOP2 protein and disrupts TOP2 from interacting with DNA, resulting in no DNA cleavage. It has low cytotoxicity, but strongly inhibits cancer cell proliferation and xenograft growth. T60 also inhibits androgen receptor activity and prostate cancer cell growth. These results indicate that T60 is a promising candidate compound that can be further developed into new anticancer drugs.

DockingApp RF: A State-of-the-Art Novel Scoring Function for Molecular Docking in a User-Friendly Interface to AutoDock Vina

Motivation: Bringing a new drug to the market is expensive and time-consuming. To cut the costs and time, computer-aided drug design (CADD) approaches have been increasingly included in the drug discovery pipeline. However, despite traditional docking tools show a good conformational space sampling ability, they are still unable to produce accurate binding affinity predictions. This work presents a novel scoring function for molecular docking seamlessly integrated into DockingApp, a user-friendly graphical interface for AutoDock Vina. The proposed function is based on a random forest model and a selection of specific features to overcome the existing limits of Vina’s original scoring mechanism. A novel version of DockingApp, named DockingApp RF, has been developed to host the proposed scoring function and to automatize the rescoring procedure of the output of AutoDock Vina, even to nonexpert users. Results: By coupling intermolecular interaction, solvent accessible surface area features and Vina’s energy terms, DockingApp RF’s new scoring function is able to improve the binding affinity prediction of AutoDock Vina. Furthermore, comparison tests carried out on the CASF-2013 and CASF-2016 datasets demonstrate that DockingApp RF’s performance is comparable to other state-of-the-art machine-learning- and deep-learning-based scoring functions. The new scoring function thus represents a significant advancement in terms of the reliability and effectiveness of docking compared to AutoDock Vina’s scoring function. At the same time, the characteristics that made DockingApp appealing to a wide range of users are retained in this new version and have been complemented with additional features.

RORγ Structural Plasticity and Druggability

Retinoic acid receptor-related orphan receptor γ (RORγ) is a transcription factor regulating the expression of the pro-inflammatory cytokine IL-17 in human T helper 17 (Th17) cells. Activating RORγ can induce multiple IL-17-mediated autoimmune diseases but may also be useful for anticancer therapy. Its deep immunological functions make RORɣ an attractive drug target. Over 100 crystal structures have been published describing atomic interactions between RORɣ and agonists and inverse agonists. In this review, we focus on the role of dynamic properties and plasticity of the RORɣ orthosteric and allosteric binding sites by examining structural information from crystal structures and simulated models. We discuss the possible influences of allosteric ligands on the orthosteric binding site. We find that high structural plasticity favors the druggability of RORɣ, especially for allosteric ligands.

Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discovery

Drug discovery is a rigorous process that requires billion dollars of investments and decades of research to bring a molecule "from bench to a bedside". While virtual docking can significantly accelerate the process of drug discovery, it ultimately lags the current rate of expansion of chemical databases that already exceed billions of molecular records. This recent surge of small molecules availability presents great drug discovery opportunities, but also demands much faster screening protocols. In order to address this challenge, we herein introduce Deep Docking (DD), a novel deep learning platform that is suitable for docking billions of molecular structures in a rapid, yet accurate fashion. The DD approach utilizes quantitative structure-activity relationship (QSAR) deep models trained on docking scores of subsets of a chemical library to approximate the docking outcome for yet unprocessed entries and, therefore, to remove unfavorable molecules in an iterative manner. The use of DD methodology in conjunction with the FRED docking program allowed rapid and accurate calculation of docking scores for 1.36 billion molecules from the ZINC15 library against 12 prominent target proteins and demonstrated up to 100-fold data reduction and 6000-fold enrichment of high scoring molecules (without notable loss of favorably docked entities). The DD protocol can readily be used in conjunction with any docking program and was made publicly available.

Computer-Aided Ligand Discovery for Estrogen Receptor Alpha

Breast cancer (BCa) is one of the most predominantly diagnosed cancers in women. Notably, 70% of BCa diagnoses are Estrogen Receptor α positive (ERα+) making it a critical therapeutic target. With that, the two subtypes of ER, ERα and ERβ, have contrasting effects on BCa cells. While ERα promotes cancerous activities, ERβ isoform exhibits inhibitory effects on the same. ER-directed small molecule drug discovery for BCa has provided the FDA approved drugs tamoxifen, toremifene, raloxifene and fulvestrant that all bind to the estrogen binding site of the receptor. These ER-directed inhibitors are non-selective in nature and may eventually induce resistance in BCa cells as well as increase the risk of endometrial cancer development. Thus, there is an urgent need to develop novel drugs with alternative ERα targeting mechanisms that can overcome the limitations of conventional anti-ERα therapies. Several functional sites on ERα, such as Activation Function-2 (AF2), DNA binding domain (DBD), and F-domain, have been recently considered as potential targets in the context of drug research and discovery. In this review, we summarize methods of computer-aided drug design (CADD) that have been employed to analyze and explore potential targetable sites on ERα, discuss recent advancement of ERα inhibitor development, and highlight the potential opportunities and challenges of future ERα-directed drug discovery.

QSAR without borders

Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.

Advances in the computational development of androgen receptor antagonists

The androgen receptor is a ligand-dependent transcriptional factor and an essential therapeutic target for prostate cancer. Competitive binding of antagonists to the androgen receptor can alleviate aberrant activation of the androgen receptor in prostate cancer. In recent years, computer-aided drug design has played an essential part in the discovery of novel androgen receptor antagonists. This review summarizes the recent advances in the discovery of novel androgen receptor antagonists through computer-aided drug design approaches; and discusses the applications of molecular modeling techniques to understand the resistance mechanisms of androgen receptor antagonists at the molecular level.

Computer-Aided Discovery of Small Molecule Inhibitors of Thymocyte Selection-Associated High Mobility Group Box Protein (TOX) as Potential Therapeutics for Cutaneous T-Cell Lymphomas

Cutaneous T-cell lymphomas (CTCL) are the most common primary lymphomas of the skin. We have previously identified thymocyte selection-associated high mobility group (HMG) box protein (TOX) as a promising drug target in CTCL; however, there are currently no small molecules able to directly inhibit TOX. We aimed to address this unmet opportunity by developing anti-TOX therapeutics with the use of computer-aided drug discovery methods. The available NMR-resolved structure of the TOX protein was used to model its DNA-binding HMG-box domain. To investigate the druggability of the corresponding protein–DNA interface on TOX, we performed a pilot virtual screening of 200,000 small molecules using in silico docking and identified ‘hot spots’ for drug-binding on the HMG-box domain. We then performed a large-scale virtual screening of 7.6 million drug-like compounds that were available from the ZINC15 database. As a result, a total of 140 top candidate compounds were selected for subsequent in vitro validation. Of those, 18 small molecules have been characterized as selective TOX inhibitors.