Computer-aided Drug Discovery Research Articles

Hit discovery of potential CDK8 inhibitors and analysis of amino acid mutations for cancer therapy through computer-aided drug discovery

Cyclin-dependent kinase 8 (CDK8) has emerged as a promising target for inhibiting cancer cell function, intensifying efforts towards the development of CDK8 inhibitors as potential cancer therapeutics. Mutations in CDK8, a protein kinase, are also implicated as a primary factor associated with tumor formation. In this study, we identified potential inhibitors through virtual screening for CDK8 and single amino acid mutations in CDK8, namely D173A (Aspartate 173 mutate to Alanine), D189N (Aspartate 189 mutate to Asparagine), T196A (Threonine 196 mutate to Alanine) and T196D (Threonine 196 mutate to Aspartate). Four databases (CHEMBEL, ZINC, MCULE, and MolPort) containing 65,209,131 molecules have been searched to identify new inhibitors for CDK8 and its single mutations. In the first step, structure-based pharmacophore modeling in the Pharmit server was used to select the compounds to know the inhibitors. Then molecules with better predicted drug-like molecule properties were selected. The final filter used to select more effective inhibitors among the previously selected molecules was molecular docking. Finally, 13 hits for CDK8, 11 hits for D173A, 11 hits for D189N, 15 hits for T196A, and 12 hits for T196D were considered potential inhibitors. A majority of the virtual screening hits exhibited satisfactorily predict pharmacokinetic characteristics and toxicity properties.

GLDM: hit molecule generation with constrained graph latent diffusion model.

Discovering hit molecules with desired biological activity in a directed manner is a promising but profound task in computer-aided drug discovery. Inspired by recent generative AI approaches, particularly Diffusion Models (DM), we propose Graph Latent Diffusion Model (GLDM)-a latent DM that preserves both the effectiveness of autoencoders of compressing complex chemical data and the DM's capabilities of generating novel molecules. Specifically, we first develop an autoencoder to encode the molecular data into low-dimensional latent representations and then train the DM on the latent space to generate molecules inducing targeted biological activity defined by gene expression profiles. Manipulating DM in the latent space rather than the input space avoids complicated operations to map molecule decomposition and reconstruction to diffusion processes, and thus improves training efficiency. Experiments show that GLDM not only achieves outstanding performances on molecular generation benchmarks, but also generates samples with optimal chemical properties and potentials to induce desired biological activity.

Developing an Improved Cycle Architecture for AI-Based Generation of New Structures Aimed at Drug Discovery.

Drug discovery involves a crucial step of optimizing molecules with the desired structural groups. In the domain of computer-aided drug discovery, deep learning has emerged as a prominent technique in molecular modeling. Deep generative models, based on deep learning, play a crucial role in generating novel molecules when optimizing molecules. However, many existing molecular generative models have limitations as they solely process input information in a forward way. To overcome this limitation, we propose an improved generative model called BD-CycleGAN, which incorporates BiLSTM (bidirectional long short-term memory) and Mol-CycleGAN (molecular cycle generative adversarial network) to preserve the information of molecular input. To evaluate the proposed model, we assess its performance by analyzing the structural distribution and evaluation matrices of generated molecules in the process of structural transformation. The results demonstrate that the BD-CycleGAN model achieves a higher success rate and exhibits increased diversity in molecular generation. Furthermore, we demonstrate its application in molecular docking, where it successfully increases the docking score for the generated molecules. The proposed BD-CycleGAN architecture harnesses the power of deep learning to facilitate the generation of molecules with desired structural features, thus offering promising advancements in the field of drug discovery processes.

A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets

Structure-based generative chemistry is essential in computer-aided drug discovery by exploring a vast chemical space to design ligands with high binding affinity for targets. However, traditional in silico methods are limited by computational inefficiency, while machine learning approaches face bottlenecks due to auto-regressive sampling. To address these concerns, we have developed a conditional deep generative model, PMDM, for 3D molecule generation fitting specified targets. PMDM consists of a conditional equivariant diffusion model with both local and global molecular dynamics, enabling PMDM to consider the conditioned protein information to generate molecules efficiently. The comprehensive experiments indicate that PMDM outperforms baseline models across multiple evaluation metrics. To evaluate the applications of PMDM under real drug design scenarios, we conduct lead compound optimization for SARS-CoV-2 main protease (Mpro) and Cyclin-dependent Kinase 2 (CDK2), respectively. The selected lead optimization molecules are synthesized and evaluated for their in-vitro activities against CDK2, displaying improved CDK2 activity.

GxVAEs: Two Joint VAEs Generate Hit Molecules from Gene Expression Profiles

The de novo generation of hit-like molecules that show bioactivity and drug-likeness is an important task in computer-aided drug discovery. Although artificial intelligence can generate molecules with desired chemical properties, most previous studies have ignored the influence of disease-related cellular environments. This study proposes a novel deep generative model called GxVAEs to generate hit-like molecules from gene expression profiles by leveraging two joint variational autoencoders (VAEs). The first VAE, ProfileVAE, extracts latent features from gene expression profiles. The extracted features serve as the conditions that guide the second VAE, which is called MolVAE, in generating hit-like molecules. GxVAEs bridge the gap between molecular generation and the cellular environment in a biological system, and produce molecules that are biologically meaningful in the context of specific diseases. Experiments and case studies on the generation of therapeutic molecules show that GxVAEs outperforms current state-of-the-art baselines and yield hit-like molecules with potential bioactivity and drug-like properties. We were able to successfully generate the potential molecular structures with therapeutic effects for various diseases from patients’ disease profiles.

In silico identification of multi-target inhibitors from medicinal fungal metabolites against the base excision repair pathway proteins of African swine fever virus.

African swine fever virus (ASFV) has emerged as a serious threat to the pork industry resulting in significant economic losses and heightened concerns about food security. With no known cure presently available, existing control measures center on animal quarantine and culling. Considering the severity and challenges posed by ASFV, it is imperative to discover new treatment strategies and implement additional measures to prevent its further spread. This study recognized the potential of 1830 fungal metabolites from medicinal fungi as antiviral compounds against base excision repair (BER) proteins of ASFV, specifically ASFVAP, ASFVPolX, and ASFVLig. A wide array of computer-aided drug discovery techniques were employed to carry out the virtual screening process: ADMET profiling revealed 319 molecules with excellent bioavailability and toxicity properties; consensus docking identified the 10 best-scoring ligands against all targets; molecular dynamics simulation elucidated the stability of the protein-ligand complexes; and MM/PB(GB)SA energy calculations predicted the binding energies of the compounds as well as the key residues integral to binding. Through in silico methods, we identified two theoretical lead candidates against ASFVAP, four against ASFVLig, and five against ASFVPolX. Two compounds, methyl ganoderate E and antcamphin M, exhibited potential multi-target inhibitory characteristics against ASFVPolX and ASFVLig, while compound cochlactone A showed promising antagonistic results against all three BER proteins. It is recommended to prioritize these hit compounds in future in vitro and in vivo studies to validate their potential as antiviral drugs against ASFV.

Clustering of small molecules: new perspectives and their impact on natural product lead discovery

The clustering of small molecules is of considerable importance for computer-aided drug discovery and virtual screening applications. The structure of chemical data in appropriate subspaces of the chemical space is relevant to sample datasets in a representative manner, to generate small libraries with wide or narrow chemical coverage (depending on the specific goals), and to guide the selection of subsets of in silico hits that are submitted for experimental confirmation. In the field of natural products, identifying regions of the chemical space where bioactive compounds congregate and understanding the relationship between biosynthetic gene clusters and the molecular structure of secondary metabolites may have a direct impact on natural product discovery and engineering. Here, we briefly discuss general approximations and available resources for the clustering of small molecules, and how the clustering of small molecules can be boosted by the application of novel clustering approximations, namely subspace clustering and multi-view clustering, which represent opposite philosophies of the clustering paradigm. We present some specific applications of small molecule clustering in the field of natural products, and analyze how a chemogenomic perspective may be particularly embodied in the field of natural products.

Computer-aided drug discovery of c-Abl kinase inhibitors from plant compounds against chronic myeloid leukemia

Chronic myeloid leukemia (CML) is a hematological malignancy characterized by the neoplastic transformation of hematopoietic stem cells, driven by the Philadelphia (Ph) chromosome resulting from a translocation between chromosomes 9 and 22. This Ph chromosome harbors the breakpoint cluster region (BCR) and the Abelson (ABL) oncogene (BCR-ABL1) which have a constitutive tyrosine kinase activity. However, the tyrosine kinase activity of BCR-ABL1 have been identified as a key player in CML initiation and maintenance through c-Abl kinase. Despite advancements in tyrosine kinase inhibitors, challenges such as efficacy, safety concerns, and recurring drug resistance persist. This study aims to discover potential c-Abl kinase inhibitors from plant compounds with anti-leukemic properties, employing drug-likeness assessment, molecular docking, in silico pharmacokinetics (ADMET) screening, density function theory (DFT), and molecular dynamics simulations (MDS). Out of 58 screened compounds for drug-likeness, 44 were docked against c-Abl kinase. The top hit compound (isovitexin) and nilotinib (control drug) were subjected to rigorous analyses, including ADMET profiling, DFT evaluation, and MDS for 100 ns. Isovitexin demonstrated a notable binding affinity (-15.492 kcal/mol), closely comparable to nilotinib (-16.826 kcal/mol), showcasing a similar binding pose and superior structural stability and reactivity. While these findings suggest isovitexin as a potential c-Abl kinase inhibitor, further validation through urgent in vitro and in vivo experiments is imperative. This research holds promise for providing an alternative avenue to address existing CML treatment and management challenges. Communicated by Ramaswamy H. Sarma

Graph Attention Site Prediction (GrASP): Identifying Druggable Binding Sites Using Graph Neural Networks with Attention.

Identifying and discovering druggable protein binding sites is an important early step in computer-aided drug discovery, but it remains a difficult task where most campaigns rely on a priori knowledge of binding sites from experiments. Here, we present a binding site prediction method called Graph Attention Site Prediction (GrASP) and re-evaluate assumptions in nearly every step in the site prediction workflow from data set preparation to model evaluation. GrASP is able to achieve state-of-the-art performance at recovering binding sites in PDB structures while maintaining a high degree of precision which will minimize wasted computation in downstream tasks such as docking and free energy perturbation.

Machine Learning-Based Virtual Screening of Antibacterial Agents against Methicillin-Susceptible and Resistant Staphylococcus aureus.

The application of computer-aided drug discovery (CADD) approaches has enabled the discovery of new antimicrobial therapeutic agents in the past. The high prevalence of methicillin-resistantStaphylococcus aureus(MRSA) strains promoted this pathogen to a high-priority pathogen for drug development. In this sense, modern CADD techniques can be valuable tools for the search for new antimicrobial agents. We employed a combination of a series of machine learning (ML) techniques to select and evaluate potential compounds with antibacterial activity against methicillin-susceptible S. aureus (MSSA) and MRSA strains. In the present study, we describe the antibacterial activity of six compounds against MSSA and MRSA reference (American Type Culture Collection (ATCC)) strains as well as two clinical strains of MRSA. These compounds showed minimal inhibitory concentrations (MIC) in the range from 12.5 to 200 μM against the different bacterial strains evaluated. Our results constitute relevant proven ML-workflow models to distinctively screen for novel MRSA antibiotics.

GraphLambda: Fusion Graph Neural Networks for Binding Affinity Prediction.

Predicting the binding affinity of protein-ligand complexes is crucial for computer-aided drug discovery (CADD) and the identification of potential drug candidates. The deep learning-based scoring functions have emerged as promising predictors of binding constants. Building on recent advancements in graph neural networks, we present graphLambda for protein-ligand binding affinity prediction, which utilizes graph convolutional, attention, and isomorphism blocks to enhance the predictive capabilities. The graphLambda model exhibits superior performance across CASF16 and CSAR HiQ NRC benchmarks and demonstrates robustness with respect to different types of train-validation set partitions. The development of graphLambda underscores the potential of graph neural networks in advancing binding affinity prediction models, contributing to more effective CADD methodologies.

Targeted Affinity Purification and Mechanism of Action of Angiotensin-Converting Enzyme (ACE) Inhibitory Peptides from Sea Cucumber Gonads.

Protein hydrolysates from sea cucumber (Apostichopus japonicus) gonads are rich in active materials with remarkable angiotensin-converting enzyme (ACE) inhibitory activity. Alcalase was used to hydrolyze sea cucumber gonads, and the hydrolysate was separated by the ultrafiltration membrane to produce a low-molecular-weight peptide component (less than 3 kDa) with good ACE inhibitory activity. The peptide component (less than 3 kDa) was isolated and purified using a combination method of ACE gel affinity chromatography and reverse high-performance liquid chromatography. The purified fractions were identified by liquid chromatography-tandem mass spectrometry (LC-MS/MS), and the resulting products were filtered using structure-based virtual screening (SBVS) to obtain 20 peptides. Of those, three noncompetitive inhibitory peptides (DDQIHIF with an IC50 value of 333.5 μmol·L-1, HDWWKER with an IC50 value of 583.6 μmol·L-1, and THDWWKER with an IC50 value of 1291.8 μmol·L-1) were further investigated based on their favorable pharmacochemical properties and ACE inhibitory activity. Molecular docking studies indicated that the three peptides were entirely enclosed within the ACE protein cavity, improving the overall stability of the complex through interaction forces with the ACE active site. The total free binding energies (ΔGtotal) for DDQIHIF, HDWWKER, and THDWWKER were -21.9 Kcal·mol-1, -71.6 Kcal·mol-1, and -69.1 Kcal·mol-1, respectively. Furthermore, a short-term assay of antihypertensive activity in spontaneously hypertensive rats (SHRs) revealed that HDWWKER could significantly decrease the systolic blood pressure (SBP) of SHRs after intravenous administration. The results showed that based on the better antihypertensive activity of the peptide in SHRs, the feasibility of targeted affinity purification and computer-aided drug discovery (CADD) for the efficient screening and preparation of ACE inhibitory peptide was verified, which provided a new idea of modern drug development method for clinical use.

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.

EGFR Kinase Inhibiting Amino-enones for Breast Cancer; CADD Approach.

The Computer-Aided Drug Discovery (CADD) approach was used to develop a few Epidermal Growth Factor Receptor (EGFR) kinase inhibitors. EGFR kinase expression is highly associated with genomic instability, higher proliferation, lower hormone receptor levels, and HER2 over-expression. It is more common in breast cancer. Thus, EGFR Kinase is one of the main targets in discovering new cancer medicine. To computationally validate some amides substituted β-amino enones as EGFR inhibitors and to carry out associated in vitro anticancer agents. We used tools such as molecular docking, MD simulations, DFT calculations, and ADMET predictions in silico to establish a preliminary SAR. In vitro, we used BT474 (ER+HER2+) and MCF-7 (ER-HER2) cell lines along with normal breast cell epithelial cells (MFC-10a) for anticancer studies and EGFR kinase inhibition assay studies. As the Reactive Oxygen Species (ROS) plays the main role in cancer development, we also analyzed the antioxidant potentials of these compounds. Among the family of eleven amides substituted (Z)-β-amino enones (5a-k), compounds 5b, 5c, 5g, and 5h showed valuable in silico and in vitro bio-activity. Remarkably, the in-silico results almost coincided with in vitro study results. We recommend compounds 5b, 5c, 5g, and 5h for pre-clinical and clinical evaluation to establish them as future cancer therapeutics.

Application of Deep Learning Neural Networks in Computer-aided Drug Discovery: A Review

Abstract: Computer-aided drug design has an important role in drug development and design. It has become a thriving area of research in the pharmaceutical industry to accelerate the drug discovery process. Deep learning, a subdivision of artificial intelligence, is widely applied to advance new drug development and design opportunities. This article reviews the recent technology that uses deep learning techniques to ameliorate the understanding of drug-target interactions in computer-aided drug discovery based on the prior knowledge acquired from various literature. In general, deep learning models can be trained to predict the binding affinity between the protein-ligand complexes and protein structures or generate protein-ligand complexes in structure-based drug discovery. In other words, artificial neural networks and deep learning algorithms, especially graph convolutional neural networks and generative adversarial networks, can be applied to drug discovery. Graph convolutional neural network effectively captures the interactions and structural information between atoms and molecules, which can be enforced to predict the binding affinity between protein and ligand. Also, the ligand molecules with the desired properties can be generated using generative adversarial networks.

Evaluating the Relative Efficacy of Synthetic and Natural Drugs in Endometriosis Adopting Molecular Modelling Approach

Background: Endometriosis is a chronic inflammatory condition of high incidence and with serious consequences. Several synthetic compounds proved to be useful in treating its symptoms by inhibiting aromatase, which is responsible for the pathogenesis of this painful illness. Nevertheless, synthetic drugs inflict several side effects, including headaches, osteoporosis, and so on. This scenario advocates the search for therapeutic formulations based on natural compounds. Thus, the present study was hypothesized to evaluate the comparative efficacy of the synthetic and natural drugs used in endometriosis, using the bioinformatics approach. Methods: CB-Dock was employed to perform molecular docking of the aromatase enzyme with two synthetic and three natural drugs for predicting their molecular interactions, and binding affinities. The curcumin-aromatase complex was further subjected to MD simulations to determine its stability, and to apply it to natural compound-based computer-aided drug discovery. Results: Curcumin was observed to dock with a greater binding interaction with aromatase. The RMSD profile, hydrogen bonds, and the RMSF and Rg values of the complex were stabilised after 50 ns, which was an indicator of the stable binding pose of the curcumin-aromatase complex. Conclusion: These in-silico findings are the basis for proposing that curcumin can be considered as a potential binding agent to inhibit the aromatase enzyme in the treatment of endometriosis. Molecular modelling and dynamics results suggest that curcumin and aromatase form a stable complex and that curcumin can be targeted as a drug in the treatment of endometriosis

AEGNN-M:A 3D Graph-Spatial Co-Representation Model for Molecular Property Prediction.

Improving the drug development process can expedite the introduction of more novel drugs that cater to the demands of precision medicine. Accurately predicting molecular properties remains a fundamental challenge in drug discovery and development. Currently, a plethora of computer-aided drug discovery (CADD) methods have been widely employed in the field of molecular prediction. However, most of these methods primarily analyze molecules using low-dimensional representations such as SMILES notations, molecular fingerprints, and molecular graph-based descriptors. Only a few approaches have focused on incorporating and utilizing high-dimensional spatial structural representations of molecules. In light of the advancements in artificial intelligence, we introduce a 3D graph-spatial co-representation model called AEGNN-M, which combines two graph neural networks, GAT and EGNN. AEGNN-M enables learning of information from both molecular graphs representations and 3D spatial structural representations to predict molecular properties accurately. We conducted experiments on seven public datasets, three regression datasets and 14 breast cancer cell line phenotype screening datasets, comparing the performance of AEGNN-M with state-of-the-art deep learning methods. Extensive experimental results demonstrate the satisfactory performance of the AEGNN-M model. Furthermore, we analyzed the performance impact of different modules within AEGNN-M and the influence of spatial structural representations on the model's performance. The interpretability analysis also revealed the significance of specific atoms in determining particular molecular properties.

Computer-aided drug discovery of natural antiviral metabolites as potential SARS-CoV-2 helicase inhibitors

In our quest to discover effective inhibitors against severe acute respiratory syndrome coronavirus 2 helicase, a diverse set of more than 300 naturally occurring antiviral metabolites was investigated. Employing advanced computational techniques, we initiated the selection process by analyzing and comparing the co-crystallized ligand (VXG) of the severe acute respiratory syndrome coronavirus 2 helicase protein (PDB ID: 5RMM) to identify compounds with structurally similar features and potential for comparable binding. Through structural similarity and pharmacophore research, 13 compounds that shared important characteristics with VXG were pinpointed. Subsequently, these candidates were subjected to molecular docking to identify seven compounds that demonstrated favorable energy profiles and accurate binding to the severe acute respiratory syndrome coronavirus 2 helicase. Among these, mycophenolic acid emerged as the most promising candidate. To ensure the safety and viability of the selected compounds, we conducted ADMET tests, which confirmed the favorable characteristics of mycophenolic acid, and the safety of atropine and plumbagin. Building on these results, we performed additional analyses on mycophenolic acid, including various molecular dynamics simulations. These investigations demonstrated that mycophenolic acid exhibited optimal binding to the severe acute respiratory syndrome coronavirus 2 helicase, maintaining flawless dynamics throughout the simulations. Furthermore, the Molecular Mechanics Poisson–Boltzmann Surface Area tests provided strong evidence that mycophenolic acid successfully formed a stable connection with the severe acute respiratory syndrome coronavirus 2 helicase, with a calculated free energy value of −294 kJ mol−1. These encouraging findings provide a solid foundation for further research, including in vitro and in vivo studies, on the three identified compounds. The potential efficacy of these compounds as treatment options for coronavirus-19 warrants further exploration and may hold significant promise in the ongoing fight against the pandemic.

Data insights from a Moroccan phytochemical database (MPDB) derived from aromatic & medicinal plants.

The geographical location of Morocco and the diversity of its topography ensure a high variability of climate conditions, ranging from humid to Saharan, and extending through subhumid, arid, and semi-arid stages. This variability offers a high floristic diversity, while the medical use of these phytochemicals has not been fully explored. Advanced computer-aided drug discovery utilizes chemical biology to accelerate the study of phytochemicals at the molecular level and discover novel therapeutic pathways. Currently, there is no online resource for phytochemicals in Morocco. Therefore, it is of interest to describe the Moroccan Phytochemicals Database (MPDB), accessible, featuring over 600 phytochemicals derived from journal articles and other reports. The web interface of the database, which is simple and easy to use, provides each phytochemical's reference, plant sources, 3D structures, and all related information. Furthermore, we provide direct links to commercially available analogs from Mcule. In addition, we provide the results of the first virtual screening against cardiovascular targets. We present these data to facilitate further exploration and exploitation of Morocco's rich phytochemical resources, and to contribute to the global understanding and application of these compounds in the medical and scientific communities.

Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis.

In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADDs predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open-Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. This paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery.