Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors.

Computational approaches in immune-oncology therapies focus on using data-driven methods to identify potential immune targets and develop novel drug candidates. In particular, the search for PD-1/PD-L1 immune checkpoint inhibitors (ICIs) has enlivened the field, leveraging the use of cheminformatics and bioinformatics tools to analyze large datasets of molecules, gene expression and protein-protein interactions. Up to now, there is still an unmet clinical need for improved ICIs and reliable predictive biomarkers. In this review, we highlight the computational methodologies applied to discovering and developing PD-1/PD-L1 ICIs for improved cancer immunotherapies with a greater focus in the last five years. The use of computer-aided drug design structure- and ligand-based virtual screening processes, molecular docking, homology modeling and molecular dynamics simulations methodologies essential for successful drug discovery campaigns focusing on antibodies, peptides or small-molecule ICIs are addressed. A list of recent databases and web tools used in the context of cancer and immunotherapy has been compilated and made available, namely regarding a general scope, cancer and immunology. In summary, computational approaches have become valuable tools for discovering and developing ICIs. Despite significant progress, there is still a need for improved ICIs and biomarkers, and recent databases and web tools have been compiled to aid in this pursuit.

Predicting Antifouling Activity and Acetylcholinesterase Inhibition of Marine-Derived Compounds Using a Computer-Aided Drug Design Approach.

Biofouling is the undesirable growth of micro- and macro-organisms on artificial water-immersed surfaces, which results in high costs for the prevention and maintenance of this process (billion €/year) for aquaculture, shipping and other industries that rely on coastal and off-shore infrastructure. To date, there are still no sustainable, economical and environmentally safe solutions to overcome this challenging phenomenon. A computer-aided drug design (CADD) approach comprising ligand- and structure-based methods was explored for predicting the antifouling activities of marine natural products (MNPs). In the CADD ligand-based method, 141 organic molecules extracted from the ChEMBL database and literature with antifouling screening data were used to build the quantitative structure-activity relationship (QSAR) classification model. An overall predictive accuracy score of up to 71% was achieved with the best QSAR model for external and internal validation using test and training sets. A virtual screening campaign of 14,492 MNPs from Encinar's website and 14 MNPs that are currently in the clinical pipeline was also carried out using the best QSAR model developed. In the CADD structure-based approach, the 125 MNPs that were selected by the QSAR approach were used in molecular docking experiments against the acetylcholinesterase enzyme. Overall, 16 MNPs were proposed as the most promising marine drug-like leads as antifouling agents, e.g., macrocyclic lactam, macrocyclic alkaloids, indole and pyridine derivatives.

Recent Advances in Application of Computer-Aided Drug Design in Anti-Influenza A Virus Drug Discovery.

Influenza A is an acute respiratory infectious disease caused by the influenza A virus, which seriously threatens global human health and causes substantial economic losses every year. With the emergence of new viral strains, anti-influenza drugs remain the most effective treatment for influenza A. Research on traditional, innovative small-molecule drugs faces many challenges, while computer-aided drug design (CADD) offers opportunities for the rapid and effective development of innovative drugs. This literature review describes the general process of CADD, the viral proteins that play an essential role in the life cycle of the influenza A virus and can be used as therapeutic targets for anti-influenza drugs, and examples of drug screening of viral target proteins by applying the CADD approach. Finally, the main limitations of current CADD strategies in anti-influenza drug discovery and the field's future directions are discussed.

Application of Computational Biology and Artificial Intelligence in Drug Design.

Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, we propose a methodology for integrating various computational techniques into new drug discovery and design.

Bridging the Gap in Malaria Parasite Resistance, Current Interventions, and the Way Forward from in Silico Perspective: A Review.

The past decade has seen most antimalarial drugs lose their clinical potency stemming from parasite resistance. Despite immense efforts by researchers to mitigate this global scourge, a breakthrough is yet to be achieved, as most current malaria chemotherapies suffer the same fate. Though the etiology of parasite resistance is not well understood, the parasite's complex life has been implicated. A drug-combination therapy with artemisinin as the central drug, artemisinin-based combination therapy (ACT), is currently the preferred malaria chemotherapy in most endemic zones. The emerging concern of parasite resistance to artemisinin, however, has compromised this treatment paradigm. Membrane-bound Ca2+-transporting ATPase and endocytosis pathway protein, Kelch13, among others, are identified as drivers in plasmodium parasite resistance to artemisinin. To mitigate parasite resistance to current chemotherapy, computer-aided drug design (CADD) techniques have been employed in the discovery of novel drug targets and the development of small molecule inhibitors to provide an intriguing alternative for malaria treatment. The evolution of plasmepsins, a class of aspartyl acid proteases, has gained tremendous attention in drug discovery, especially the non-food vacuole. They are expressed at multi-stage of the parasite's life cycle and involve in hepatocytes' egress, invasion, and dissemination of the parasite within the human host, further highlighting their essentiality. In silico exploration of non-food vacuole plasmepsin, PMIX and PMX unearthed the dual enzymatic inhibitory mechanism of the WM382 and 49c, novel plasmepsin inhibitors presently spearheading the search for potent antimalarial. These inhibitors impose structural compactness on the protease, distorting the characteristic twist motion. Pharmacophore modeling and structure activity of these compounds led to the generation of hits with better affinity and inhibitory prowess towards PMIX and PMX. Despite these headways, the major obstacle in targeting PM is the structural homogeneity among its members and to human Cathepsin D. The incorporation of CADD techniques described in the study at early stages of drug discovery could help in selective inhibition to augment malaria chemotherapy.

Direct Keap1-kelch inhibitors as potential drug candidates for oxidative stress-orchestrated diseases: A review on In silico perspective.

The recent outcry in the search for direct keap1 inhibitors requires a quicker and more effective drug discovery process which is an inherent property of the Computer Aided Drug Discovery (CADD) to bring drug candidates into the clinic for patient's use. This Keap1 (negative regulator of ARE master activator) is emerging as a therapeutic strategy to combat oxidative stress-orchestrated diseases. The advances in computer algorithm and compound databases require that we highlight the functionalities that this technology possesses that can be exploited to target Keap1-Nrf2 PPI. Therefore, in this review, we uncover the in silico approaches that had been exploited towards the identification of keap1 inhibition in the light of appropriate fitting with relevant amino acid residues, we found 3 and 16 other compounds that perfectly fit keap1 kelch pocket/domain. Our goal is to harness the parameters that could orchestrate keap1 surface druggability by utilizing hotspot regions for virtual fragment screening and identification of hotspot residues.

Artificial Neural Networks in Computer-Aided Drug Design: An Overview of Recent Advances.

Computer-aided drug design (CADD) is the framework in which the huge amount of data accumulated by high-throughput experimental methods used in drug design is quantitatively studied. Its objectives include pattern recognition, biomarker identification and/or classification, etc. In order to achieve these objectives, machine learning algorithms and especially artificial neural networks (ANNs) have been used over ADMET factor testing and QSAR modeling evaluation. This paper provides an overview of the current trends in CADD-applied ANNs, since their use was re-boosted over a decade ago.

Computational Methodologies in the Exploration of Marine Natural Product Leads.

Computational methodologies are assisting the exploration of marine natural products (MNPs) to make the discovery of new leads more efficient, to repurpose known MNPs, to target new metabolites on the basis of genome analysis, to reveal mechanisms of action, and to optimize leads. In silico efforts in drug discovery of NPs have mainly focused on two tasks: dereplication and prediction of bioactivities. The exploration of new chemical spaces and the application of predicted spectral data must be included in new approaches to select species, extracts, and growth conditions with maximum probabilities of medicinal chemistry novelty. In this review, the most relevant current computational dereplication methodologies are highlighted. Structure-based (SB) and ligand-based (LB) chemoinformatics approaches have become essential tools for the virtual screening of NPs either in small datasets of isolated compounds or in large-scale databases. The most common LB techniques include Quantitative Structure⁻Activity Relationships (QSAR), estimation of drug likeness, prediction of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, similarity searching, and pharmacophore identification. Analogously, molecular dynamics, docking and binding cavity analysis have been used in SB approaches. Their significance and achievements are the main focus of this review.

Breakthroughs in AI and multi-omics for cancer drug discovery: A review.

Cancer is one of the biggest medical challenges we face today. It is characterized by abnormal, uncontrolled growth of cells that can spread to different parts of the body. Cancer is extremely complex, with genetic variations and the ability to adapt and evolve. This means we must continuously pursue innovative approaches to developing new cancer drugs. While traditional drug discovery methods have led to important breakthroughs, they also have significant limitations that make it difficult to efficiently create new, cost-effective cancer therapies. Integrating computational tools into the cancer drug discovery process is a major step forward. By harnessing computing power, we can overcome some of the inherent barriers of traditional methods. This review examines the range of computational techniques now being used, such as molecular docking, QSAR models, virtual screening, and pharmacophore modeling. It looks at recent advances in areas like machine learning and molecular simulations. The review also discusses the current challenges with these technologies and envisions future directions, underscoring how transformative these computational tools can be for creating targeted, new cancer treatments.

Structure-Based Identification of Novel Histone Deacetylase 4 (HDAC4) Inhibitors.

Histone deacetylases (HDACs) are important cancer drug targets. Existing FDA-approved drugs target the catalytic pocket of HDACs, which is conserved across subfamilies (classes) of HDAC. However, engineering specificity is an important goal. Herein, we use molecular modeling approaches to identify and target potential novel pockets specific to Class IIA HDAC-HDAC4 at the interface between HDAC4 and the transcriptional corepressor component protein NCoR. These pockets were screened using an ensemble docking approach combined with consensus scoring to identify compounds with a different binding mechanism than the currently known HDAC modulators. Binding was compared in experimental assays between HDAC4 and HDAC3, which belong to a different family of HDACs. HDAC4 was significantly inhibited by compound 88402 but not HDAC3. Two other compounds (67436 and 134199) had IC50 values in the low micromolar range for both HDACs, which is comparable to the known inhibitor of HDAC4, SAHA (Vorinostat). However, both of these compounds were significantly weaker inhibitors of HDAC3 than SAHA and thus more selective, albeit to a limited extent. Five compounds exhibited activity on human breast carcinoma and/or urothelial carcinoma cell lines. The present result suggests potential mechanistic and chemical approaches for developing selective HDAC4 modulators.

Recent advances in application of computer-aided drug design in anti-COVID-19 Virials Drug Discovery.

Corona Virus Disease 2019 (COVID-19) is a global pandemic epidemic caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), which poses a serious threat to human health worldwide and results in significant economic losses. With the continuous emergence of new virus strains, small molecule drugs remain the most effective treatment for COVID-19. The traditional drug development process usually requires several years; however, the development of computer-aided drug design (CADD) offers the opportunity to develop innovative drugs quickly and efficiently. The literature review describes the general process of CADD, the viral proteins that play essential roles in the life cycle of SARS-CoV-2 and can serve as therapeutic targets, and examples of drug screening of viral target proteins by applying CADD methods. Finally, the potential of CADD in COVID-19 therapy, the deficiency, and the possible future development direction are discussed.

Proposition of In silico Pharmacophore Models for Malaria: A Review.

In the field of medicinal chemistry, the concept of pharmacophore refers to the specific region of a molecule that possesses essential structural and chemical characteristics for binding to a receptor and eliciting biological activity. Understanding the pharmacophore is crucial for drug research and development, as it allows the design of new drugs. Malaria, a widespread disease, is commonly treated with chloroquine and artemisinin, but the emergence of parasite resistance limits their effectiveness. This study aims to explore computer simulations to discover a specific pharmacophore for Malaria, providing new alternatives for its treatment. A literature review was conducted, encompassing articles proposing a pharmacophore for Malaria, gathered from the "Web of Science" database, with a focus on recent publications to ensure up-to-date analysis. The selected articles employed diverse methods, including ligand-based and structurebased approaches, integrating molecular structure and biological activity data to yield comprehensive analyses. Affinity evaluation between the proposed pharmacophore and the target receptor involved calculating free energy to quantify their interaction. Multiple linear regression was commonly utilized, though it is sensitive to multicollinearity issues. Another recurrent methodology was the use of the Schrödinger package, employing tools such as the Phase module and the OPLS force field for interaction analysis. Pharmacophore model proposition allows threedimensional representations guiding the synthesis and design of new biologically active compounds, offering a promising avenue for discovering therapeutic agents to combat Malaria.

Recent Trends in Computer-aided Drug Design for Anti-cancer Drug Discovery.

Cancer is considered one of the deadliest diseases globally, and continuous research is being carried out to find novel potential therapies for myriad cancer types that affect the human body. Researchers are hunting for innovative remedies to minimize the toxic effects of conventional therapies being driven by cancer, which is emerging as pivotal causes of mortality worldwide. Cancer progression steers the formation of heterogeneous behavior, including self-sustaining proliferation, malignancy, and evasion of apoptosis, tissue invasion, and metastasis of cells inside the tumor with distinct molecular features. The complexity of cancer therapeutics demands advanced approaches to comprehend the underlying mechanisms and potential therapies. Precision medicine and cancer therapies both rely on drug discovery. In vitro drug screening and in vivo animal trials are the mainstays of traditional approaches for drug development; however, both techniques are laborious and expensive. Omics data explosion in the last decade has made it possible to discover efficient anti-cancer drugs via computational drug discovery approaches. Computational techniques such as computer-aided drug design have become an essential drug discovery tool and a keystone for novel drug development methods. In this review, we seek to provide an overview of computational drug discovery procedures comprising the target sites prediction, drug discovery based on structure and ligand-based design, quantitative structure-activity relationship (QSAR), molecular docking calculations, and molecular dynamics simulations with a focus on cancer therapeutics. The applications of artificial intelligence, databases, and computational tools in drug discovery procedures, as well as successfully computationally designed drugs, have been discussed to highlight the significance and recent trends in drug discovery against cancer. The current review describes the advanced computer-aided drug design methods that would be helpful in the designing of novel cancer therapies.

Computational approaches for the design of modulators targeting protein-protein interactions.

BACKGROUND: Protein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery. AREAS COVERED: In this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes. EXPERT OPINION: Computational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.

Characterization of prevalent tyrosine kinase inhibitors and their challenges in glioblastoma treatment.

Glioblastoma multiforme (GBM) is a highly aggressive malignant primary tumor in the central nervous system. Despite extensive efforts in radiotherapy, chemotherapy, and neurosurgery, there remains an inadequate level of improvement in treatment outcomes. The development of large-scale genomic and proteomic analysis suggests that GBMs are characterized by transcriptional heterogeneity, which is responsible for therapy resistance. Hence, knowledge about the genetic and epigenetic heterogeneity of GBM is crucial for developing effective treatments for this aggressive form of brain cancer. Tyrosine kinases (TKs) can act as signal transducers, regulate important cellular processes like differentiation, proliferation, apoptosis and metabolism. Therefore, TK inhibitors (TKIs) have been developed to specifically target these kinases. TKIs are categorized into allosteric and non-allosteric inhibitors. Irreversible inhibitors form covalent bonds, which can lead to longer-lasting effects. However, this can also increase the risk of off-target effects and toxicity. The development of TKIs as therapeutics through computer-aided drug design (CADD) and bioinformatic techniques enhance the potential to improve patients' survival rates. Therefore, the continued exploration of TKIs as drug targets is expected to lead to even more effective and specific therapeutics in the future.

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.

Drug Repurposing Targeting Pseudomonas aeruginosa MvfR Using Docking, Virtual Screening, Molecular Dynamics, and Free-Energy Calculations.

Pseudomonas aeruginosa is an opportunistic Gram-negative bacterium responsible for acute and chronic infections in planktonic state or in biofilms. The sessile structures are known to confer physical stability, increase virulence, and work as a protective armor against antimicrobial compounds. P. aeruginosa can control the expression of genes, population density, and biofilm formation through a process called quorum sensing (QS), a rather complex and hierarchical system of communication. A recent strategy to try and overcome bacterial resistance is to target QS proteins. In this study, a combined multi-level computational approach was applied to find possible inhibitors against P. aeruginosa QS regulator protein MvfR, also known as PqsR, using a database of approved FDA drugs, as a repurposing strategy. Fifteen compounds were identified as highly promising putative MvfR inhibitors. On those 15 MvfR ligand complexes, molecular dynamic simulations and MM/GBSA free-energy calculations were performed to confirm the docking predictions and elucidate on the mode of interaction. Ultimately, the five compounds that presented better binding free energies of association than the reference molecules (a known antagonist, M64 and a natural inducer, 2-nonyl-4-hydroxyquinoline) were highlighted as very promising MvfR inhibitors.

Machine Learning in Antibacterial Drug Design.

Advances in computer hardware and the availability of high-performance supercomputing platforms and parallel computing, along with artificial intelligence methods are successfully complementing traditional approaches in medicinal chemistry. In particular, machine learning is gaining importance with the growth of the available data collections. One of the critical areas where this methodology can be successfully applied is in the development of new antibacterial agents. The latter is essential because of the high attrition rates in new drug discovery, both in industry and in academic research programs. Scientific involvement in this area is even more urgent as antibacterial drug resistance becomes a public health concern worldwide and pushes us increasingly into the post-antibiotic era. In this review, we focus on the latest machine learning approaches used in the discovery of new antibacterial agents and targets, covering both small molecules and antibacterial peptides. For the benefit of the reader, we summarize all applied machine learning approaches and available databases useful for the design of new antibacterial agents and address the current shortcomings.

Modulation of ERCC1-XPF Heterodimerization Inhibition via Structural Modification of Small Molecule Inhibitor Side-Chains.

Inhibition of DNA repair enzymes is an attractive target for increasing the efficacy of DNA damaging chemotherapies. The ERCC1-XPF heterodimer is a key endonuclease in numerous single and double strand break repair processes, and inhibition of the heterodimerization has previously been shown to sensitize cancer cells to DNA damage. In this work, the previously reported ERCC1-XPF inhibitor 4 was used as the starting point for an in silico study of further modifications of the piperazine side-chain. A selection of the best scoring hits from the in silico screen were synthesized using a late stage functionalization strategy which should allow for further iterations of this class of inhibitors to be readily synthesized. Of the synthesized compounds, compound 6 performed the best in the in vitro fluorescence based endonuclease assay. The success of compound 6 in inhibiting ERCC1-XPF endonuclease activity in vitro translated well to cell-based assays investigating the inhibition of nucleotide excision repair and disruption of heterodimerization. Subsequently compound 6 was shown to sensitize HCT-116 cancer cells to treatment with UVC, cyclophosphamide, and ionizing radiation. This work serves as an important step towards the synergistic use of DNA repair inhibitors with chemotherapeutic drugs.

Efficacy Evaluation of SDF-1α-Based Polypeptides in an Acute Myocardial Infarction Model Using Structure-Based Drug Design.

Stromal cell-derived factor-1 alpha (SDF-1α, CXCL12) mediates the migration of circulating cells to desired sites for tissue development, homeostasis, and regeneration and can be used to promote cardiac regeneration by recruiting stem cells. However, the use of SDF-1α in the injured heart necessitates not only higher binding affinity to its receptor, CXCR4+, but also better robustness against enzymatic degradation than other SDF-1 isoforms. Here, we conduct a screening of SDF-1α analog peptides that were designed by structure-based drug design (SBDD), a type of computer-aided drug design (CADD). We have developed in vitro and in vivo methods that enable us to estimate the effect of peptides on the migration of human mesenchymal stem cells (hMSCs) and cardiac regeneration in acute myocardial infarction (AMI)-induced animals, respectively. We demonstrate that one type of SDF-1α analog peptide, SDP-4, among the four analog peptides preselected by SBDD, is more potent than native SDF-1α for cardiac regeneration in myocardial infarction. It is interesting to note that the migratory effects of SDP-4 determined by a wound healing assay, a Transwell assay, and a 2D migration assay are comparable to those of SDF-1α. These results suggest that in vivo, as well as in vitro, screening of peptides developed by SBDD is a quintessential process to the development of a novel therapeutic compound for cardiac regeneration. Our finding also has an implication that the SDP-4 peptide is an excellent candidate for use in the regeneration of an AMI heart.