Computer-aided Drug Design Research Articles

Exploring the potential of 2-arylbenzimidazole scaffolds as novel α-amylase inhibitors: QSAR, molecular docking, simulation and pharmacokinetic studies.

Previous studies have shown that 2-arylbenzimidazole derivatives have a strong anti-diabetic effect. To further explore this potential, we develop new analogues of the compound using ligand-based drug design and tested their inhibitory and binding properties through QSAR analyses, molecular docking, dynamic simulations and pharmacokinetic studies. By using quantitative structure activity relationship and ligand-based modification, a highly precise predictive model and design of potent compounds was developed from the derivatives of 2-arylbenzimidazoles. Molecular docking and simulation studies were then conducted to identify the optimal binding poses and pharmacokinetic profiles of the newly generated therapeutic drugs. DFT was employed to optimize the chemical structures of 2-arylbenzimidazole derivatives using B3LYP/6-31G* as the basis set. The model with the highest R2trng set, R2adj, Q2cv, and R2test sets (0.926, 0.912, 0.903, and 0.709 respectively) was chosen to predict the inhibitory activities of the derivatives. Five analogues designed using ligand-based strategy had higher activity than the hit molecule. Additionally, the designed molecules had more favorable MolDock scores than the hit molecule and acarbose and simulation studies confirm on their stability and binding affinities towards the protein. The ADME and druglikeness properties of the analogues indicated that they are safe to consume orally and have a high potential for total clearance. The results of this study showed that the suggested analogues could act as α-amylase inhibitors, which could be used as a basis for the creation of new drugs to treat type 2 diabetes mellitus.

Exploring the Potential Mechanisms of Guanxinshutong Capsules in Treating Pathological Cardiac Hypertrophy based on Network Pharmacology, Computer-Aided Drug Design, and Animal Experiments

Exploring the Potential Mechanisms of Guanxinshutong Capsules in Treating Pathological Cardiac Hypertrophy based on Network Pharmacology, Computer-Aided Drug Design, and Animal Experiments

A commentary on "Current perspectives and trend of Computer-Aided drug design: a review and bibliometric analysis".

A commentary on "Current perspectives and trend of Computer-Aided drug design: a review and bibliometric analysis".

Abstract LB021: BAU-243: A novel BCL-2 inhibitor inducing autophagic cell death in glioblastoma, unveiled through multistep virtual screening, comprehensive in vitro and in vivo animal model evaluation

Abstract The excessive expression of antiapoptotic members in the B-cell leukemia/lymphoma-2 (BCL-2) family proteins emerges as a noteworthy oncogenic target in cancer cells. Employing a multistep virtual screening and filtering approach that integrates structure and ligand-based drug design, we aimed to identify novel and potent BCL-2 inhibitors from a sizable small molecule database, comprising over 210,000 compounds. Subsequently, seven selected molecules underwent rigorous biological activity tests in human cancer cell lines and Time-Resolved Fluorescence Resonance Energy Transfer (TR-FRET) assays. Four of these compounds exhibited notable inhibitory effects on cancer cell proliferation and led to an increase in apoptotic cell fractions. Among the identified compounds, BAU-243 displayed a substantial affinity for Bcl-2, demonstrating efficacy in reducing glioblastoma (GBM) cell proliferation, including cancer-initiating cells. Notably, in contrast to the selective Bcl-2 inhibitor ABT-199, BAU-243 induced autophagic cell death in GBM cells. In vivo investigations using orthotopic brain tumor models revealed a significant reduction in tumor growth with BAU-243 compared to both the vehicle group and animals treated with ABT-199. Computational modeling simulations, in concordance with in vitro findings, proposed that BAU-243 activates autophagic cell death by disrupting the Beclin 1:Bcl-2 complex. This positions BAU-243 as a promising small molecule for the treatment of GBM. Citation Format: Seyma Calis, Berna Dogan, Asuman Celebi, Ozlem Yapicier, Turker Kilic, Eda Tahir Turanli, Timucin Avsar, Serdar Durdagi. BAU-243: A novel BCL-2 inhibitor inducing autophagic cell death in glioblastoma, unveiled through multistep virtual screening, comprehensive in vitro and in vivo animal model evaluation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB021.

Computer Aided Drug Design (CADD) Supported Medication: A Guide to Improved Patient Care

Computer Aided Drug Design (CADD) supported medication structure fuses a wide scope of hypothetical and computational methodologies that functions as significant component of present-day drug discovery. CADD strategies have made key commitments to the occasion of medications that are in clinical use or in clinical preliminaries. Such techniques have risen and developed together with trial approaches used in drug formulation. This incorporates different database and different instruments used in the zone of bioinformatics like BLAST, FASTA, DRUG BANK and so forth this strategy is utilized to break down, create and find drugs closely resembling biologically active molecules. The ligand-oriented computer aided drug designing helped study of ligand that are found to associate with the objective target of interest to search their 2D and 3D structures. The significance of those methods stands to theorize the nature and class of the given target. CADD methods can build the odds of perceiving the compounds with useful qualities, prompting development and extend the possibilities of getting a compound over the numerous obstacles of preclinical testing. It tends to be utilized for quick calculation of chemical libraries so as to direct and quicken the beginning period improvement of newly active compounds. It likewise incorporates a wide assortment computational methodology like virtual library, virtual screening, lead optimization and so on. Over the ongoing years, computer aided drug designing (CADD) has been otherwise called in silico screening that has risen as a compelling methodology in view of drug finding and progression through varied advanced attributes. In silico screening additionally makes an approach to combination and screen the chose mixes for new and better therapeutics. CADD and bioinformatics instruments convey advantages, for example, cost sparing, time to market, in-sight information of drug receptor interactions that accelerate drug disclosure and improvement

A Deep Dive into PDE5 Inhibition: Innovative Discoveries via Virtual Screening

Background: PDE5 inhibitors have had a surge in popularity over the last decade owing to their efficacy in the treatment of erectile dysfunction, coronary vasculopathy, and pulmonary arterial hypertension. These inhibitors exhibit competitive binding with phosphodiesterase type 5 and inhibit the hydrolysis of cyclic guanosine monophosphate, hence elevating the levels of cGMP in smooth muscle cells and prolonging the duration of an erection. However, due to production costs and side effects, further research is needed to discover new PDE5 inhibitors. background: The field of PDE5 inhibitors has risen in popularity in the past decade because of the success of PDE5 inhibitors in treating erectile dysfunction. Due to the structural identity with cGMP; PDE5 inhibitors competitively bind with PDE5 and limit cGMP hydrolysis, which raises the cGMP level in smooth muscle cells and lengthens the duration of an erection. PDE5 inhibitors were also found to be beneficial for coronary vasculopathy, pulmonary arterial hypertension, and benign prostatic hyperplasia. But, due to the expensive cost of production and unwanted side effects, it is necessary to explore the possibility of the discovery of new PDE5 inhibitors. Objective: The study aimed to identify potent PDE5 inhibitors by employing the extensive application of computer-aided drug design. objective: To identify potent PDE5 inhibitors by employing the extensive application of computer-aided drug design. Method: Three different databases, named Million Molecules Database, Natural Product Database, and NCI Database, have been screened, which has been followed by filtering based on various druglikeness rules, docking, ADME, toxicity, consensus molecular docking, and 100 ns molecular dynamics simulation. method: Three different databases named Million Molecules Database, Natural Product Database, and NCI Database has been screened followed by filtering based on various drug-likeness such as Lipinski rule of five, Ghose rule, Veber rule, and Muegge rule, docking, ADME, toxicity, consensus molecular docking, and 100 ns Molecular dynamics simulation Results: Three compounds (ZINC05351336, ZINC12030898, and ZINC17949426) have exhibited stable-binding characteristics at the active site of PDE5, demonstrating a robust binding affinity. These molecules have been found to possess drug-like capabilities, effective ADME features, low toxicity, and high stability. Conclusion: The study has delved into the realm of PDE5 inhibitors, which have been found to be effective in treating erectile dysfunction, but high production costs and side effects necessitate new ones. Through computer-aided drug design and screening, three compounds have been identified with promising binding characteristics, drug-appropriate properties, effective ADME profiles, minimal toxicity, and stability, making them potential candidates for future PDE5 inhibitors

Predictive modeling of skin permeability for molecules: Investigating FDA-approved drug permeability with various AI algorithms.

The transdermal route of drug administration has gained popularity for its convenience and bypassing the first-pass metabolism. Accurate skin permeability prediction is crucial for successful transdermal drug delivery (TDD). In this study, we address this critical need to enhance TDD. A dataset comprising 441 records for 140 molecules with diverse LogKp values was characterized. The descriptor calculation yielded 145 relevant descriptors. Machine learning models, including MLR, RF, XGBoost, CatBoost, LGBM, and ANN, were employed for regression analysis. Notably, LGBM, XGBoost, and gradient boosting models outperformed others, demonstrating superior predictive accuracy. Key descriptors influencing skin permeability, such as hydrophobicity, hydrogen bond donors, hydrogen bond acceptors, and topological polar surface area, were identified and visualized. Cluster analysis applied to the FDA-approved drug dataset (2326 compounds) revealed four distinct clusters with significant differences in molecular characteristics. Predicted LogKp values for these clusters offered insights into the permeability variations among FDA-approved drugs. Furthermore, an investigation into skin permeability patterns across 83 classes of FDA-approved drugs based on the ATC code showcased significant differences, providing valuable information for drug development strategies. The study underscores the importance of accurate skin permeability prediction for TDD, emphasizing the superior performance of nonlinear machine learning models. The identified key descriptors and clusters contribute to a nuanced understanding of permeability characteristics among FDA-approved drugs. These findings offer actionable insights for drug design, formulation, and prioritization of molecules with optimum properties, potentially reducing reliance on costly experimental testing. Future research directions include offering promising applications in pharmaceutical research and formulation within the burgeoning field of computer-aided drug design.

The Power of Molecular Dynamics Simulations and Their Applications to Discover Cysteine Protease Inhibitors.

A large family of enzymes with the function of hydrolyzing peptide bonds, called peptidases or cysteine proteases (CPs), are divided into three categories according to the peptide chain involved. CPs catalyze the hydrolysis of amide, ester, thiol ester, and thioester peptide bonds. They can be divided into several groups, such as papain-like (CA), viral chymotrypsin-like CPs (CB), papain-like endopeptidases of RNA viruses (CC), legumain-type caspases (CD), and showing active residues of His, Glu/Asp, Gln, Cys (CE). The catalytic mechanism of CPs is the essential cysteine residue present in the active site. These mechanisms are often studied through computational methods that provide new information about the catalytic mechanism and identify inhibitors. The role of computational methods during drug design and development stages is increasing. Methods in Computer-Aided Drug Design (CADD) accelerate the discovery process, increase the chances of selecting more promising molecules for experimental studies, and can identify critical mechanisms involved in the pathophysiology and molecular pathways of action. Molecular dynamics (MD) simulations are essential in any drug discovery program due to their high capacity for simulating a physiological environment capable of unveiling significant inhibition mechanisms of new compounds against target proteins, especially CPs. Here, a brief approach will be shown on MD simulations and how the studies were applied to identify inhibitors or critical information against cysteine protease from several microorganisms, such as Trypanosoma cruzi (cruzain), Trypanosoma brucei (rhodesain), Plasmodium spp. (falcipain), and SARS-CoV-2 (Mpro). We hope the readers will gain new insights and use our study as a guide for potential compound identifications using MD simulations.

492 Novel BH3 Mimetic Bcl-2 Inhibitor Promotes Autophagic Cell Death and Reduces in Vivo Glioblastoma Tumor Growth

INTRODUCTION: Anti-apoptotic members of the Bcl-2 family proteins play central roles in the regulation of cell death in glioblastoma, the most malignant type of brain tumor. Despite the advances in GBM treatment, there is still an urgent need for new therapeutic approaches. METHODS: In silico studies resulted in 500,000 molecules being screened with computer-aided drug design methods and the molecule with the highest binding affinity was selected and named BAU-243. Cell viability, IC50 value, tumor formation potential, tumor spheroid formation potential, tumor invasion studies were investigated following BAU-243 application. The molecular changes caused by Bcl-2 inhibition were demonstrated by quantitative PCR, western blot and immunofluorescence methods. In in vivo studies, glioma tumors were transplanted intracranially into Nod Scid Gamma(NSG) mice using the mouse xenograft model. RESULTS: Here, we report a novel 4-thiazolidinone derivative BH3 mimetic, BAU-243 that binds to Bcl-2 with a high affinity. BAU-243 effectively reduced overall glioblastoma cell proliferation including a subpopulation of cancer-initiating cells in contrast to the selective Bcl-2 inhibitor ABT-199. While ABT-199 successfully induces apoptosis in high BCL2-expressing neuroblastoma SHSY-5Y cells, BAU-243 triggered autophagic cell death rather than apoptosis in A172 cells, indicated by the upregulation of BECN1, ATG5, and MAP1LC3B expression. Lc3b-II, a potent autophagy marker, was significantly upregulated following BAU-243 treatment. Moreover, BAU-243 significantly reduced tumor growth in vivo in orthotopic brain tumor models when compared to the vehicle group, and ABT-199 treated animals. To elucidate the molecular mechanisms of action of BAU-243, we performed computational modeling simulations that were consistent with in vitro results. CONCLUSIONS: Results indicated that BAU-243 activates autophagic cell death by disrupting Beclin 1:Bcl-2 complex, and may serve as a potential small molecule for the treatment of glioblastoma.

Semaglutide as a Possible Calmodulin Binder: Ligand-Based Computational Analyses and Relevance to Its Associated Reward and Appetitive Behaviour Actions

Semaglutide, a glucagon-like peptide-1 (GLP-1) receptor agonist, has gained considerable attention as a therapeutic agent for type 2 diabetes mellitus and obesity. Despite its clinical success, the precise mechanisms underlying its pharmacological effects remain incompletely understood. In this study, we employed ligand-based drug design strategies to investigate potential off-target interactions of semaglutide. Through a comprehensive in silico screening of semaglutide’s structural properties against a diverse panel of proteins, we have identified calmodulin (CaM) as a putative novel target of semaglutide. Molecular docking simulations revealed a strong interaction between semaglutide and CaM, characterized by favourable binding energies and a stable binding pose. Further molecular dynamics simulations confirmed the stability of the semaglutide–CaM complex, emphasizing the potential for a physiologically relevant interaction. In conclusion, our ligand-based drug design approach has uncovered calmodulin as a potential novel target of semaglutide. This discovery sheds light on the complex pharmacological profile of semaglutide and offers a promising direction for further research into the development of innovative therapeutic strategies for metabolic disorders. The CaM, and especially so the CaMKII, system is central in the experience of both drug- and natural-related reward. It is here hypothesized that, due to semaglutide binding, the reward pathway-based calmodulin system may be activated, and/or differently regulated. This may result in the positive semaglutide action on appetitive behaviour. Further studies are required to confirm these findings.

Abstract 6343: AnTenGagerTM, a novel “2+1” T cell engager platform, enables conditional T cell activation with reduced risk of CRS

Abstract Background: CD3+ T cell engaging bispecific antibodies (TCE) redirect T cells to attack targeted tumor cells by simultaneously binding TAA on cancer cells and CD3 complex on a T cell to form a TCR-independent immune synapse. Multiple TCEs have demonstrated promising therapeutic efficacy in the treatment of hematological malignancies. In contrast, suboptimal efficacy in the treatment of solid tumors and the risk of inducing cytokine release syndrome (CRS) continue to be major challenges. Several factors, such as format, affinity, and valency of the antibody, TAA copy number, and antigen size and epitope, can impact the therapeutic efficacy and safety of a TCE. In this study, we developed a novel “2+1” TCE platform, AnTenGagerTM, which enables conditional T cell binding and activation upon TAA-crosslinking, potent anti-tumor activity, and reduced risk of CRS. Methods: Humanized CD3 antibodies were derived from mouse hybridoma, followed by affinity optimization utilizing Computer Aided Drug Design. The Fab-scFv-Fab format was used as the backbone structure of AnTenGagers. The anti-CD3 scFv sequences and the length of peptide linkers between VH/VL, scFv/CH1, and scFv/CH2 were screened and compared in different preclinical assays. LALA mutations (L234A, L235A) were introduced to abolish the Fc receptor binding capability. In a series of in vitro and/or in vivo models, the efficacy, safety and developability of AnTenGagers targeting multiple TAAs, such as Her2, GD2, CDH6, LILRB4, FLT3 and GPRC5D, were evaluated. Results: A library of humanized parental CD3 antibodies with a broad spectrum of affinities were generated, some of which cross react with monkey CD3. AnTenGagers exhibited limited binding to CD3-positive cells before TAA-crosslinking, suggesting a reduced risk of CRS that is caused by systemic CD3+T cell activation. Four CD3 sequences with varying affinities were utilized to construct AnTenGagers with identical TAA-targeting arm. Compared to clinical benchmarks targeting the same TAA, all AnTenGagers demonstrated substantially lower binding capacity to CD3+ cells in the absence of TAA-crosslinking, while inducing increased cytotoxicity against target-positive tumor cells. In addition, they induced significantly less ex-vivo cytokine release by human PBMC than benchmarks. Notably, all AnTenGagers demonstrated enhanced in vivo efficacy compared to the benchmark, exhibiting complete response (CR) in PBMC humanized MM1S mouse CDX model while the serum concentration of proinflammatory cytokines was substantially lower in groups treated with AnTenGagers. AnTenGagers also demonstrated good developability with high expression yield, good thermostability and high stability under different stress conditions. Conclusions: These results suggested that AnTenGagerTM is a promising platform for developing the next generation of TCEs with enhanced efficacy and safety. Citation Format: Bing Hou, Hui Yuwen, Tengteng Li, Yijing Ren, Ao Sun, Zaoshun Hu, Jay Mei, Bo Shan. AnTenGagerTM, a novel “2+1” T cell engager platform, enables conditional T cell activation with reduced risk of CRS [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6343.

Identification of novel potential cathepsin-B inhibitors through pharmacophore-based virtual screening, molecular docking, and dynamics simulation studies for the treatment of Alzheimer's disease.

Cathepsin B is a cysteine protease lysosomal enzyme involved in several physiological functions. Overexpression of the enzyme enhances its proteolytic activity and causes the breakdown of amyloid precursor protein (APP) into neurotoxic amyloid β (Aβ), a characteristic hallmark of Alzheimer's disease (AD). Therefore, inhibition of the enzyme is a crucial therapeutic aspect for treating the disease. Combined structure and ligand-based drug design strategies were employed in the current study to identify the novel potential cathepsin B inhibitors. Five different pharmacophore models were developed and used for the screening of the ZINC-15 database. The obtained hits were analyzed for the presence of duplicates, interfering PAINS moieties, and structural similarities based on Tanimoto's coefficient. The molecular docking study was performed to screen hits with better target binding affinity. The top seven hits were selected and were further evaluated based on their predicted ADME properties. The resulting best hits, ZINC827855702, ZINC123282431, and ZINC95386847, were finally subjected to molecular dynamics simulation studies to determine the stability of the protein-ligand complex during the run. ZINC123282431 was obtained as the virtual lead compound for cathepsin B inhibition and may be a promising novel anti-Alzheimer agent.

Abstract 1815: Optimization of Lin28 inhibitors: A promising therapeutic approach for the suppression of cancer cell proliferation, stemness properties, and spheroid growth

Abstract Background: The pluripotency factor known as Lin28 plays a crucial role in the regulation of cancer cell stem-like characteristics, hence facilitating the formation of cancer and the evolution of therapy-resistant tumors. The protein exerts its function by utilizing its cold shock domain and zinc knuckle domain (ZKD) to engage with the Let-7 pre-microRNA and impede the process of Let-7 production. The implementation of chemical agents to hinder the interaction between Lin28 and Let-7 has been proven as a therapeutic approach for treating aggressive tumors. Methods: Computer-aided drug design techniques were employed in the development of a novel series of small molecule inhibitors, which are derivatives of our previously identified lead compounds Ln115, Ln15, and Ln7. Fluorescent Polarization Assay (FP), and Electrophoretic Mobility Shift Assay (EMSA) were used to screen the chemicals as Lin28-Let7 interaction disruptors using Lin28A/B ZKD domain. To further screen the chemicals, the Incucyte cell proliferation assay was performed using two Lin28A and Lin28B positive cells, IGROV-1, and DuNE cells, respectively. The inhibition of Lin28 activity was subsequently evaluated by measuring Let-7 micro-RNA and Lin28 protein levels using RT-PCR and Western Blot. Then, the metabolism profile was explored using Microsomal Stability Assay. Incucyte proliferation assay and MTS assay determined the IC50 of lead compound in Lin28 positive cells. The anti-stemness properties of the lead compound were subsequently evaluated through assays for stem gene expression levels and colony formation. To evaluate the potential of the lead compound to further move into animal studies, we tested it on a newly developed 3D Neuroendocrine Prostate Cancer (NEPC) model. Results: In this study, we provide a novel set of derivatives, within which, Ln268 was chosen as the lead compound. Ln268 shows higher metabolic stability with a half-life time of 462 min and clearance rate of 5 ul/min/mg, compared to its father compound at 347 min and 46 ul/min/mg, respectively. Ln268 treatment resulted in a time-dependent inhibition of cell growth with an IC50 of 2.5 uM at 72 hrs. Ln268 reduces mRNA levels of stemness and neurodifferentiation markers such as CHGA, CHG, SYP, SCGN, OCT3/4, NANOG, ID4, FOXC1, ALDH1A2, and FOXO3. In the 3D NEPC model, Ln268 suppresses spheroid growth in a dose- and time-dependent manner. Ln268 achieved 100% spheroid growth inhibition at the concentration of 100 uM, while the current commercially available C1632 only reached 50%. Conclusion: We discovered a novel Ln15 derivative named Ln268, which has a better metabolism profile and increased inhibitory activity to distrust Lin28-Let7 interaction and cancer cell proliferation. Ln268 has the potential to be improved and used as an anti-cancer treatment for a variety of malignancies, such as prostate cancer. Citation Format: Victor M. Matias-Barrios, Mariia Radaeva, Graciella Rosellinny, Artem Cherkasov, Xuesen Dong. Optimization of Lin28 inhibitors: A promising therapeutic approach for the suppression of cancer cell proliferation, stemness properties, and spheroid growth [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1815.

Discovery of DNL343: A Potent, Selective, and Brain-Penetrant eIF2B Activator Designed for the Treatment of Neurodegenerative Diseases.

Eukaryotic translation initiation factor 2B (eIF2B) is a key component of the integrated stress response (ISR), which regulates protein synthesis and stress granule formation in response to cellular insult. Modulation of the ISR has been proposed as a therapeutic strategy for treatment of neurodegenerative diseases such as vanishing white matter (VWM) disease and amyotrophic lateral sclerosis (ALS) based on its ability to improve cellular homeostasis and prevent neuronal degeneration. Herein, we report the small-molecule discovery campaign that identified potent, selective, and CNS-penetrant eIF2B activators using both structure- and ligand-based drug design. These discovery efforts culminated in the identification of DNL343, which demonstrated a desirable preclinical drug profile, including a long half-life and high oral bioavailability across preclinical species. DNL343 was progressed into clinical studies and is currently undergoing evaluation in late-stage clinical trials for ALS.

Current perspectives and trend of computer-aided drug design: a review and bibliometric analysis.

Computer-aided drug design (CADD) is a drug design technique for computing ligand‒receptor interactions and is involved in various stages of drug development. To better grasp the frontiers and hotspots of CADD, we conducted a review analysis through bibliometrics. A systematic review of studies published between 2000 and July 20, 2023 was conducted following the PRISMA guidelines. Literature on CADD was selected from the Web of Science Core Collection. General information, publications, output trends, countries/regions, institutions, journals, keywords, and influential authors were visually analysed using software such as Excel, VOSviewer, RStudio, and CiteSpace. A total of 2,031 publications were included. These publications primarily originated from 99 countries or regions, led by the U.S. and China. Among the contributors, MacKerell AD had the highest number of articles and greatest influence. The Journal of Medicinal Chemistry was the most cited journal, whereas the Journal of Chemical Information and Modeling had the highest number of publications. Influential authors in the field were identified. Current research shows active collaboration between countries, institutions, and companies. CADD technologies such as homology modelling, pharmacophore modelling, quantitative conformational relationships, molecular docking, molecular dynamics simulation, binding free energy prediction, and high-throughput virtual screening can effectively improve the efficiency of new drug discovery. Artificial intelligence-assisted drug design and screening based on CADD represent key topics direction for future development. Furthermore, this paper will be helpful for better understanding the frontiers and hotspots of CADD.

The AI-driven Drug Design (AIDD) platform: an interactive multi-parameter optimization system integrating molecular evolution with physiologically based pharmacokinetic simulations.

Computer-aided drug design has advanced rapidly in recent years, and multiple instances of in silico designed molecules advancing to the clinic have demonstrated the contribution of this field to medicine. Properly designed and implemented platforms can drastically reduce drug development timelines and costs. While such efforts were initially focused primarily on target affinity/activity, it is now appreciated that other parameters are equally important in the successful development of a drug and its progression to the clinic, including pharmacokinetic properties as well as absorption, distribution, metabolic, excretion and toxicological (ADMET) properties. In the last decade, several programs have been developed that incorporate these properties into the drug design and optimization process and to varying degrees, allowing for multi-parameter optimization. Here, we introduce the Artificial Intelligence-driven Drug Design (AIDD) platform, which automates the drug design process by integrating high-throughput physiologically-based pharmacokinetic simulations (powered by GastroPlus) and ADMET predictions (powered by ADMET Predictor) with an advanced evolutionary algorithm that is quite different than current generative models. AIDD uses these and other estimates in iteratively performing multi-objective optimizations to produce novel molecules that are active and lead-like. Here we describe the AIDD workflow and details of the methodologies involved therein. We use a dataset of triazolopyrimidine inhibitors of the dihydroorotate dehydrogenase from Plasmodium falciparum to illustrate how AIDD generates novel sets of molecules.

Insights into the selective mechanism of PDE2/9a inhibitors from silico aspects

The selective design of competitive enzyme inhibitors is an extremely difficult task but necessary work for certain types of systems, such as the phosphodiesterase (PDE) system addressed in this article. In the PDE family, PDE2A and PDE9 respectively target the central nervous system and heart failure, and share many conserved amino acids at their binding sites. Therefore, gaining a deep understanding of the selective mechanisms of PDE2A/9A is crucial for designing highly selective drugs. In this study, various computer-aided drug design (CADD) methods, including molecular docking, molecular dynamics simulations (MD), and binding free energy calculations, are employed to explore the selective mechanisms of PDE2A/9A. Overall, our research results indicate a selective design strategy for PDE2A, which involves incorporating hydrophobic or aromatic moieties into the molecular structure to better accommodate the hydrophobic pocket of PDE2A. Additionally, it is recommended to introduce functional groups capable of forming connections with selective residues, such as Phe830 and Gln812 for PDE2A, or Ala452 and Tyr424 for PDE9A, to enhance the selectivity of inhibitors targeting PDE2A/9A. This achievement is anticipated to pave the way for the development of innovative and selective small molecules targeting PDE2A/9A. Communicated by Ramaswamy H. Sarma

Discovery of macrocyclic CDK2/4/6 inhibitors with improved potency and DMPK properties through a highly efficient macrocyclic drug design platform

Cyclin-dependent kinases (CDKs) are critical cell cycle regulators that are often overexpressed in tumors, making them promising targets for anti-cancer therapies. Despite substantial advancements in optimizing the selectivity and drug-like properties of CDK inhibitors, safety of multi-target inhibitors remains a significant challenge. Macrocyclization is a promising drug discovery strategy to improve the pharmacological properties of existing compounds. Here we report the development of a macrocyclization platform that enabled the highly efficient discovery of a novel, macrocyclic CDK2/4/6 inhibitor from an acyclic precursor (NUV422). Using dihedral angle scan and structure-based, computer-aided drug design to select an optimal ring-closing site and linker length for the macrocycle, we identified compound 8 as a potent new CDK2/4/6 inhibitor with optimized cellular potency and safety profile compared to NUV422. Our platform leverages both experimentally-solved as well as generative chemistry-derived macrocyclic structures and can be deployed to streamline the design of macrocyclic new drugs from acyclic starting compounds, yielding macrocyclic compounds with enhanced potency and improved drug-like properties.

Molecular docking and molecular dynamic simulation-based phytoconstituents against SARS-CoV-2 with dual inhibition of the primary protease targets

A novel coronavirus has caused major health problems and is spreading globally. The main protease enzyme plays a significant role in the number of copies of ss-RNA produced during the proteolytic cleavage of polypeptides. This work aims to find possible dual inhibitors of the 3-Chymotrypsin-like proteases PDB-6W63 and 6LU7 which increase efficiency and faster inhibition activity. By using an in-silico technique, polyphenols are molecularly docked against these targets to inhibit protease enzymes. Some polyphenols, such as pelargonidin and naringin, have significant dual inhibition characteristics with remarkable binding affinities with active scaffolds of both proteins, which have important ADMET parameters. These organic molecules are strongly bonded with amino acids of protein via mostly hydrogen bonding. These polyphenols also have outstanding docking scores and MMGBSA energies. The validity of the docking score was evaluated using a molecular dynamics simulation that assessed the stability of the complex. With the aid of computer-aided drug design, we hypothesise that the dual inhibition of compounds pelargonidin and naringin could effectively and potentially oppose SARS-CoV-2.

Recent Advances in Automated Structure-Based De Novo Drug Design.

As the number of determined and predicted protein structures and the size of druglike 'make-on-demand' libraries soar, the time-consuming nature of structure-based computer-aided drug design calls for innovative computational algorithms. De novo drug design introduces in silico heuristics to accelerate searching in the vast chemical space. This review focuses on recent advances in structure-based de novo drug design, ranging from conventional fragment-based methods, evolutionary algorithms, and Metropolis Monte Carlo methods to deep generative models. Due to the historical limitation of de novo drug design generating readily available drug-like molecules, we highlight the synthetic accessibility efforts in each category and the benchmarking strategies taken to validate the proposed framework.