Identification Of Lead Compounds Research Articles

Advances in Protein-Ligand Binding Affinity Prediction via Deep Learning: A Comprehensive Study of Datasets, Data Preprocessing Techniques, and Model Architectures.

Drug discovery is a complex and expensive procedure involving several timely and costly phases through which new potential pharmaceutical compounds must pass to get approved. One of these critical steps is the identification and optimization of lead compounds, which has been made more accessible by the introduction of computational methods, including deep learning (DL) techniques. Diverse DL model architectures have been put forward to learn the vast landscape of interaction between proteins and ligands and predict their affinity, helping in the identification of lead compounds. This survey fills a gap in previous research by comprehensively analyzing the most commonly used datasets and discussing their quality and limitations. It also offers a comprehensive classification of the most recent DL methods in the context of protein-ligand binding affinity prediction (BAP), providing a fresh perspective on this evolving field. We thoroughly examine commonly used datasets for BAP and their inherent characteristics. Our exploration extends to various preprocessing steps and DL techniques, including graph neural networks, convolutional neural networks, and transformers, which are found in the literature. We conducted extensive literature research to ensure that the most recent deep learning approaches for BAP were included by the time of writing this manuscript. The systematic approach used for the present study highlighted inherent challenges to BAP via DL, such as data quality, model interpretability, and explainability, and proposed considerations for future research directions. We present valuable insights to accelerate the development of more effective and reliable DL models for BAP within the research community. The present study can considerably enhance future research on predicting affinity between protein and ligand molecules, hence further improving the overall drug development process.

Comprehensive applications of the artificial intelligence technology in new drug research and development.

Target-based strategy is a prevalent means of drug research and development (R&D), since targets provide effector molecules of drug action and offer the foundation of pharmacological investigation. Recently, the artificial intelligence (AI) technology has been utilized in various stages of drug R&D, where AI-assisted experimental methods show higher efficiency than sole experimental ones. It is a critical need to give a comprehensive review of AI applications in drug R &D for biopharmaceutical field. Relevant literatures about AI-assisted drug R&D were collected from the public databases (Including Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, Springer, and ScienceDirect) through a keyword searching strategy with the following terms [("Artificial Intelligence" OR "Knowledge Graph" OR "Machine Learning") AND ("Drug Target Identification" OR "New Drug Development")]. In this review, we first introduced common strategies and novel trends of drug R&D, followed by characteristic description of AI algorithms widely used in drug R&D. Subsequently, we depicted detailed applications of AI algorithms in target identification, lead compound identification and optimization, drug repurposing, and drug analytical platform construction. Finally, we discussed the challenges and prospects of AI-assisted methods for drug discovery. Collectively, this review provides comprehensive overview of AI applications in drug R&D and presents future perspectives for biopharmaceutical field, which may promote the development of drug industry.

Insights on exploring the therapeutic potential and structural modification of Tetrandrine.

Tetrandrine (Tet), a bisbenzylisoquinoline alkaloid from Stephania tetrandra, is noted for its diverse pharmacological effects but faces limitations in clinical use due to toxicity, poor solubility, and low bioavailability. Researchers are working to address these issues by developing Tet derivatives with greater therapeutic potential through structural modification. Generally, key modifications include: 1) introducing an aromatic heterocycle or a hydrophobic alkyne unit at the C-5 position can enhance its antitumor activity; 2) adding an amide, sulfonamide, or electron-withdrawing group at the C-14 position can enhance its antitumor activity; 3) changing its structure to a quaternary ammonium salt can alter its solubility and greatly boost its antibacterial activity; 4) structural modification of the C-12-methoxybenzyl motif can enhance its metabolic stability and thus change the activity of the analogs; 5) Tet structural simplification may result in the identification of anticancer lead compounds with novel mechanisms of action. This review systematically summarizes these modification strategies and evaluates the biological activities of Tet derivatives, aiming to guide further optimization and facilitate the discovery of lead analogs with improved efficacy. The future direction and possibility of Tet structural optimization are also considered.

Identification of novel PAD2 inhibitors using pharmacophore-based virtual screening, molecular docking, and MD simulation studies

In the realm of epigenetic regulation, Protein arginine deiminase 2 (PAD2) stands out as a therapeutic target due to its significant role in neurological disorders, rheumatoid arthritis (RA), multiple sclerosis (MS), and various cancers. To date, no in silico studies have focused on PAD2 for lead compound identification. Therefore, we conducted structure-based pharmacophore modeling, virtual screening, molecular docking, molecular dynamics (MD) simulations, and essential dynamics studies using PCA and free energy landscape analyses to identify repurposed drugs and selective inhibitors against PAD2. The best pharmacophore model, ‘Pharm_01,’ had a selectivity score of 10.485 and an excellent ROC curve quality of 0.972. Pharm1 consisted of three hydrogen bond donors (HBD) and two hydrophobic (Hy) features (DDDHH). A virtual screening of about 9.2 million compounds yielded 2575 hits using a fit value threshold of 2.5 and drug-likeness criteria. Molecular docking identified the top ten molecules, which were verified using MD simulations. Stability was verified using MM-PBSA studies, whereas conformational differences were investigated using PCA and free energy landscape analyses. Two hits (Leads 1 and 2) from the DrugBank dataset showed promise for repurposing as PAD2 inhibitors, while one hit compound (Lead 8) from the ZINC database emerged as a novel PAD2 inhibitor. These findings indicate that the discovered compounds may be potent PAD2 inhibitors, necessitating additional preclinical and clinical research to produce viable treatments for cancer and neurological disorders.

Advances in Research on Semi-Synthesis, Biotransformation and Biological Activities of Novel Derivatives from Maslinic Acid

Since ancient times, humans have turned to medicinal plants for treating various ailments and curing specific diseases, as these natural plants serve as the primary source of a range of phytochemicals, including triterpenes. Maslinic acid (MA), also known as (2α,3β)-2,3-dihydroxyolean-12-en-28-oic acid, is a pentacyclic triterpene acid present in numerous plants including olive, known for its high safety profile in humans. Recent experimental data increasingly suggests that MA exhibits diverse biological properties and therapeutic effects on various organ diseases, highlighting its significant potential for clinical applications due to its diverse potential pharmacological activities that promote health and resist various diseases, such as hypoglycemic, neuroprotective, anti-tumor, anti-inflammatory, antioxidant and multiple other biological activities. However, the undesirable pharmacokinetic properties of MA, such as high lipophilicity, pose a limitation to its application and development, impacting its bioavailability. Consequently, extensive research spanning decades has focused on structurally modifying MA to overcome these limitations and enhance its pharmacokinetic and therapeutic characteristics, leading to the identification of several potential lead compounds. In this review, we focus on the progress of research in recent years on MA with interest to its chemical and enzymatic modifications as well as the relationships between the modified structures or derivatives and their biological activities.

Structure-Based Virtual Screening and Protein–Protein Docking Analysis of ERBB2 and Associated Proteins for Pediatric Cancer Therapeutic Approaches

Abstract Introduction The Erythroblastosis Oncogene B homolog 2 (ERBB2) protein, also known as human epidermal growth factor receptor 2 (HER2), is a key player in cancer growth, especially in neuroblastoma and gastric cancers. Targeting ERBB2 has led to successful therapies, making it an important focus in cancer research with the potential to improve treatment for HER2-positive cancers. Objective The primary goal of this research is to employ a multifaceted computational approach to identify potential drug candidates targeting ERBB2. We aim to combine virtual screening, protein–protein docking, and functional partner prediction to provide insights into the molecular interactions and potential efficacy of the identified compounds. Additionally, we intend to assess the safety profiles of these compounds using advanced toxicity prediction tools. Methods Relevant protein sequence and structural data for ERBB2 and epidermal growth factor receptor (EGFR) were sourced from publicly available databases. Potential inhibitors from the Enamine and LifeChemicals databases were identified through virtual screening using AutoDock Vina. Functional partners of ERBB2 were explored using STRING, KEGG, and REACTOME servers. The identified compounds were subjected to toxicity prediction using the ProTox-II server. Results Virtual screening led to the selection of 10 compounds with favorable binding energies (–8.346 to –6.296 kcal/mol) and specific amino acid interactions (Thr5, Arg412, Leu414, and Ser441) with the receptor. On the other hand, EGFR was identified as the best functional partner for ERBB2. The EGFR residues Gln408, Lys463, Phe412, and Asp436 found key residues for the complex formation. The toxicity prediction analysis revealed that the majority of compounds exhibited acceptable safety profiles, although a subset of compounds showed lower prediction scores, suggesting the need for further consideration. Conclusion This comprehensive computational approach, integrating virtual screening, protein–protein docking, functional partner identification, and toxicity prediction, offers a systematic framework for efficient drug discovery. The identification of potential lead compounds targeting ERBB2, with emphasis on both binding affinity and safety, underscores the significance of such an approach in streamlining the drug development process. By prioritizing compounds with promising efficacy, functional relevance, and acceptable toxicity profiles, this study advances our understanding of potential therapeutic agents, enhancing the likelihood of successful translation from computational predictions to real-world drug candidates.

SmartCADD: AI-QM Empowered Drug Discovery Platform with Explainability.

Artificial intelligence (AI) has emerged as a pivotal force in enhancing productivity across various sectors, with its impact being profoundly felt within the pharmaceutical and biotechnology domains. Despite AI's rapid adoption, its integration into scientific research faces resistance due to myriad challenges: the opaqueness of AI models, the intricate nature of their implementation, and the issue of data scarcity. In response to these impediments, we introduce SmartCADD, an innovative, open-source virtual screening platform that combines deep learning, computer-aided drug design (CADD), and quantum mechanics methodologies within a user-friendly Python framework. SmartCADD is engineered to streamline the construction of comprehensive virtual screening workflows that incorporate a variety of formerly independent techniques─spanning ADMET property predictions, de novo 2D and 3D pharmacophore modeling, molecular docking, to the integration of explainable AI mechanisms. This manuscript highlights the foundational principles, key functionalities, and the unique integrative approach of SmartCADD. Furthermore, we demonstrate its efficacy through a case study focused on the identification of promising lead compounds for HIV inhibition. By democratizing access to advanced AI and quantum mechanics tools, SmartCADD stands as a catalyst for progress in pharmaceutical research and development, heralding a new era of innovation and efficiency.

Bioassay-directed isolation of antiplasmodial alkaloids and lignans from the dry seeds of Piper guineense Schumach. & Thonn. (Piperaceae) and their cytotoxicity

The identification of antiplasmodial lead compounds from the dry seeds of Piper guineense has been performed using a bioassay-directed protocol. Briefly, its methanolic seeds extract has been suspended in water and partitioned using n-hexane, dichloromethane, ethyl acetate and n-butanol to afford four main fractions A−D which were further submitted to antiplasmodial assay against Plasmodium falciparum 3D7. The bioassay results showed that its dichloromethane-soluble fraction has the highest potency with 31.8 % of the parasite P. falciparum 3D7 survival. The purification of the active fraction with successive column chromatography led to the isolation and identification of eleven compounds including six piperine-type amides (1−6), two lignans (7 and 8), one fatty acid (9), and two steroids (10 and 11). The antiplasmodial assay of compounds 1−8 against the two strains Pf 3D7 and Pf Dd2 showed that only the alkaloids 1−6 displayed moderate potencies with the highest IC50 values of 17.4 ± 0.7 µg/mL (for 6 against Pf 3D7) and 14.6 ± 0.3 µg/mL (for 2 against Pf Dd2). Furthermore, the cytotoxicity of the tested compounds against Vero cell lines showed that only compounds 5 and 6 displayed a cytotoxicity with CC50 values of 335.4 ± 7.6 µg/mL and 151.8 ± 2.4 µg/mL, respectively, while their selectivity index limits an acceptable range. The results support the use of the plant in folk medicine for handling malaria, and demonstrate that piperine-type amides are responsible for the antiplasmodial activity of P. guineense and are good leads in the development of safe and potent antimalarial drugs.

Chemical optimization and derivatization of micrococcin p2 to target multiple bacterial infections: new antibiotics from thiopeptides.

Antimicrobial resistance poses a significant threat to humanity, and the development of new antibiotics is urgently needed. Our research has focused on thiopeptide antibiotics such as micrococcin P2 (MP2) and derivatives thereof as new anti-infective agents. Thiopeptides are sulfur-rich, structurally complex substances that exhibit potent activity against Gram-positive pathogens and Mycobacteria species, including clinically resistant strains. The clinical development of thiopeptides has been hampered by the lack of efficient synthetic platforms to conduct detailed structure-activity relationship studies of these natural products. The present contribution touches upon efficient synthetic routes to MP2 that laid the groundwork for clinical translation. The medicinal chemistry campaign on MP2 has been guided by computational molecular dynamic simulations and parallel investigations to improve drug-like properties, such as enhancing the aqueous solubility and optimizing antibacterial activity. Such endeavors have enabled identification of promising lead compounds, AJ-037 and AJ-206, against Mycobacterium avium complex (MAC). Extensive in vitro studies revealed that these compounds exert potent activity against MAC species, a subspecies of non-tuberculous mycobacteria (NTM) that proliferate inside macrophages. Two additional pre-clinical candidates have been identified: AJ-024, for the treatment of Clostridioides difficile infections, and AJ-147, for methicillin-resistant Staphylococcus aureus impetigo. Both compounds compare quite favorably with current first-line treatments. In particular, the ability of AJ-147 to downregulate pro-inflammatory cytokines adds a valuable dimension to its clinical use. In light of above, these new thiopeptide derivatives are well-poised for further clinical development.

Elucidation of the solution structure of macrocyclic paritaprevir and exploration of its antitumor potential through molecular docking

Macrocyclic molecules have emerged as significant contenders in drug development and supramolecular chemistry. Paritaprevir is a 15-membered macrocycle approved for the treatment of chronic Hepatitis C virus infection. The structural flexibility inherent in paritaprevir enables the presentation of diverse conformational features in solutions, thereby influencing its binding pattern with proteins. In this paper, we investigated the structural characteristics of paritaprevir in protic solvents (methanol and trifluoroethanol) as well as an aprotic solvent (acetonitrile) by using experimental electronic circular dichroism spectra combined with density functional theory calculations. Our study reveals that paritaprevir can adopt different molecular conformations due to its flexible carbon chain and the solvent environment. Conformer B is particularly stable in polar solvents because of the planar hydrophobic interactions between the phenanthridine and methylpyrazine groups. Density functional theory calculations-assisted structural analyses further disclosed the weak noncovalent interactions within the stable conformations of paritaprevir. Molecular docking of paritaprevir on different cancer proteins showed that the macrocycle was highly selective against lymphoma, endometrial carcinoma, colorectal cancer, and breast cancer. This suggests that paritaprevir could potentially serve as a promising lead compound for drug design targeting these specific types of cancers. The comparison between the low energy conformation and pharmacophore conformation (docking conformation) indicates that conformer B is the primary pharmacophore conformation of paritaprevir, providing important macrocyclic structural characteristics for future macrocyclic drug development.

Ethnomedicinal Uses, Phytochemistry, Pharmacology, and Toxicology of Ruellia tuberosa L.: A Review.

Natural Products continue to be the purest source of physiologically active molecules employed in the identification of possible lead compounds in the drug discovery process. Acanthaceae is a big plant family with around 2500 species, found primarily in subtropical and tropical regions, as well as the Mediterranean, Australia, and the United States. Several species of the Acanthaceae family have been used traditionally to treat a variety of diseases, including gastrointestinal and cardiovascular ailments, etc. Ruellia tuberosa commonly known as "Mexican Bluebell" is a perennial herb that originated in Central America and has spread to some countries in the Southern tropics and Southeast Asia. It has been utilized as a traditional Rasayana plant from ancient times. R. tuberosa extracts and phytochemicals showed potent bioactivities, such as anticancer, anti-inflammatory, wound healing, antifungal, antimicrobial, anti-diabetic, hypoglycemic, hypolipidemic, gastro-protective, and anthelminthic activities. Chemical analyses have unveiled a range of bioactive constituents within the plant, including alkaloids, flavonoids, saponins, and phenolic compounds, suggestive of its therapeutic potential. Collectively, this review provides an overview of R. tuberosa, encompassing its traditional uses, ethnomedicinal importance, phytochemistry, pharmacological properties, and toxicity.

1,2,3-Triazole – [1,2,4]triazolo[3,4-b][1,3,4]thiadiazine hybrids: A switch for improvement of antibreast cancer activity targeting epidermal growth factor receptor

The development of anticancer agents targeting EGFR represents a promising strategy in the field of medicinal chemistry. Consequently, a novel series of 1,2,3-triazole – [1,2,4]triazolo[3,4-b][1,3,4]thiadiazine hybrids (7a-i &8a-f) were designed, synthesized, and screened for their in vitro anticancer activity against three human breast cancer cell lines, MCF-7, MDA-MB-231, and MDA-MB-415. Notably, hybrids 7f and 7h, featuring 4-fluorophenyl and 3,5-dichlorophenyl substitutions, respectively, exhibited superior activity against MCF-7 and MDA-MB-231 cell lines, and comparable activity against MDA-MB-415 cell line, compared to the standard drug Erlotinib. Toxicity studies on the noncancerous breast cell line MCF-10A indicate that these compounds are selective for cancer cell lines. Furthermore, these compounds demonstrated high efficacy in the in vitro EGFR inhibition assay, with IC50 values of 0.419 ± 0.05 μM and 0.312 ± 0.02 μM, respectively, in comparison to Erlotinib (IC50: 0.421 ± 0.03 μM). In silico experiments, including molecular docking studies to elucidate the interaction mode with the EGFR active site and ADMET analysis to verify drug-likeness properties, were also conducted for the potent compounds. Both experimental and in silico investigations suggest that compounds 7f and 7h hold promise as lead compounds for further drug design and development endeavors.

Identification of Potential Lead Compounds against BCL-2 through In-silico Screening of Phytochemicals of Nigella sativa and Cuscuta reflexa for Oral Squamous Cell Carcinoma Management

Significance of the study: In silico analysis is frequently used in physicochemical characterisation, the discovery and improvement of novel compounds with affinity to a target, and the elucidation of absorption, distribution, metabolism, excretion, and toxicity features. Aim and Objective: The aim of this study is to assess the efficacy of Nigella sativa and Cuscuta reflexa in targeting Bcl2-antiapoptotic protein in Oral Squamous Cell Carcinoma through in silico analysis. Materials and Methods: The present study was designed to formulate a drug against Oral Squamous cell carcinoma against the target protein Bcl-2. Protein Database (PDB) was used to select and analyse the protein. Based on the literature search, the original molecules selected were thymoquinone, tannin pyrogallol, Cuscutin, kaempferol, nigellicine and the ligand structures were obtained ZINC15 database. Target protein structure was recognised through Protein sequence from PB (Protein Data Bank). Swiss ADME was used for assessing the properties of the molecules. Twenty new molecules were identified from zinc pharmer and docking was done for all the 20 using Swiss dock. Binding energy was assessed and compared with the original molecules.ZINC73690300 and ZINC73690304 had better binding energy than that of original molecules. Both molecules can be potential candidates for Bcl2 inhibition to prevent OSCC. Results: ZINC73690300 and ZINC73690304 had better binding energy than that of the original molecules. Both molecules can be potential candidates for Bcl-2 inhibition to prevent OSCC. Conclusion: The present study evaluated the binding energy and bioavailability of the combined extract, thus attempting to predict the target ligand binding between the compounds in OSCC before attempting in vitro studies.-

Synthesis and biological evaluation of panaxadiol ester derivatives possessing pyrazole and pyrrole moiety as HIF-1α inibitors

Hypoxia-inducing factor-1α (HIF-1α) is overexpressed in variety of tumor patients and plays an important role in the regulation of hypoxia response in tumor cells. Therefore, its inhibitors have become one of the targets for the treatment of a variety of cancers. Two series of panaxadiol (PD) ester derivatives containing pyrazole (18a–j) and pyrrole (19a–n) moiety were synthesized and their HIF-1α inhibitory activities were evaluated. Among all the target compouds, compounds 18c, 19d, and 19n (IC50 = 8.70–10.44 μM) showed better HIF-1α inhibitory activity than PD (IC50 = 13.35 μM). None of these compounds showed cytotoxicity above 100 μM and inhibited HIF-1α transcription in a dose-dependent manner. These compounds showed good antitumor activity and provide lead compounds for further design and activity study of PD ester derivatives.

Naturally occurring benzophenones and xanthones from Garcinia smeathmannii (Planch. & Triana) Oliv. displayed anti-inflammatory effects by modulating the activities of inflammatory mediators in LPS-stimulated RAW 264.7 macrophages.

Introduction: There is a growing interest in studying natural products for the identification of novel lead compounds for drug development for treating inflammatory diseases. Although some studies have focused anti-inflammatory activity of benzophenones and xanthones, exploring additional targets such as enzymes and cytokines, involved in their inflammatory response could provide more comprehensive understanding of the compounds' anti-inflammatory effects. In this study, four xanthones ananixanthone (1), smeathxanthone A (2), smeathxanthone B (3), and 1,3,5,8-tetrahydroxy-2-(3-methybut-2-enyl)-4-(3,7-dimethyloct-2,6-dienyl) xanthone (4); and three benzophenones guttiferone O (5), guttiferone M (6), and aristophenone A (7) from Garcinia smeathmannii (Planch. & Triana) Oliv. were investigated for their effect on nitric oxide production, cyclooxygenase, lipoxygenase inhibition, and Th1/Th2 cytokines production in activated RAW 264.7 macrophages. Methods: The Griess reagent method and the ferrous oxidation-xylenol orange assay were used to evaluate the inhibition of NO production and the 15-lipoxygenase activity respectively. Cyclooxygenase activity was assessed using the fluorometric COX activity assay kit and measurement of Th1/Th2 cytokines was performed using a flow cytometer. Results: All the tested compounds exhibited a dose-dependent inhibition of NO production with varying degrees of inhibitory effects on 15-LOX activity. Compound (6), displays the best inhibitory effect on COX-1/COX-2 activity. A general trend of the tested compounds on cytokines profiles revealed that compound (5) showed a pronounced enhancement of anti-inflammatory cytokines (IL-4 and IL-10). Conclusion: This observation supports future exploration of ananixanthone (1), guttiferone O (5), and guttiferone (6) as potential candidates for the development of anti-inflammatory drugs.

Identifying Potential SOS1 Inhibitors via Virtual Screening of Multiple Small Molecule Libraries against KRAS-SOS1 Interaction.

The RAS-MAPK signaling pathway, crucial for cell proliferation and differentiation, involves key proteins KRAS and SOS1. Mutations in the KRAS and SOS1 genes are implicated in various cancer types, including pancreatic, lung, and juvenile myelomonocytic leukemia. There is considerable interest in identifying inhibitors targeting KRAS and SOS1 to explore potential therapeutic strategies for cancer treatment. In this study, advanced in silico techniques were employed to screen small molecule libraries at this interface, leading to the identification of promising lead compounds as potential SOS1 inhibitors. Comparative analysis of the average binding free energies of these predicted potent compounds with known SOS1 small molecule inhibitors revealed that the identified compounds display similar or even superior predicted binding affinities compared to the known inhibitors. These findings offer valuable insights into the potential of these compounds as candidates for further development as effective anti-cancer agents.

An in silico approach for identification of lead compound as FtsZ inhibitor.

The remarkable conservation of the FtsZ among Gram-positive and Gram-negative bacteria, a crucial GTPase in bacterial cell division, has emerged as a promising antibacterial drug target to combat antibacterial resistance. There have been several coordinated efforts to develop inhibitors against FtsZ which can also serve as potential candidates for future antibiotics. In the present study, a natural product-like library (≈50,000 compounds) was employed to conduct HTVS against Staphylococcus aureus FtsZ protein (PDB Id: 6KVP). Additionally, molecular docking was carried out in two modes, SP and XP docking, using the Schrödinger suite. The glide scores of ligands obtained by XP docking were further summarized and compared with the control ligands (ZI1- co-crystal and PC190723-a compound undergoing clinical trial). Using the Prime-MM-GBSA approach, BFE calculations were performed on the top XP-scored ligands (≈598 compounds). These hits were also evaluated for ADMET parameters using the Qikprop algorithm, SwissADME, and in silico carcinogenicity testing using Carcinopred-El. Based on the results, ligand 4-FtsZ complex was considered for the 300ns MDS analysis to get insights into its binding modes within the catalytic pocket of FtsZ protein. The analysis revealed that the amide linkage sandwiched between the triazole and 1-oxa-8-azaspirodecan-8-ium moiety (Val203) as well as the aminoethyl group present at 1st position on the triazole moiety (Leu209, Leu200, Asp210, and Ala202) were responsible for the FtsZ inhibitory activity, owing to their crucial interactions with key amino acid residues. Further, the complex also displayed good protein-ligand stability, ultimately predicting ligand 4 as a potent lead compound for the inhibition of FtsZ. Thus, our in silico findings will serve as a framework for in-depth in-vitro and in-vivo investigations encouraging the development of FtsZ inhibitors as a new generation of antibacterial agents.

In silico Analysis of 1,3,4-Oxadiazoles as Potential BCL-2 Inhibitor for Cancer Treatment

The possible efficacy of 1,3,4-oxadiazoles as B-cell lymphoma 2 (BCL-2) inhibitors for cancer treatment is investigated in this study using in silico approaches such as molecular docking, ADME and toxicity prediction. Molecular docking studies predict the binding affinities and binding modes of a series of 1,3,4-oxadiazole derivatives with the BCL-2 protein. The results revealed that the compounds with strong interactions and favourable binding modes, indicating their potential as BCL-2 inhibitors. An ADMET analysis assesses the pharmacokinetic properties and potential toxicity of the identified compounds. Parameters such as absorption, distribution, metabolism, excretion and toxicity (ADMET) were evaluated to predict the suitability of these 1,3,4-oxadiazoles as drug candidates for cancer therapy. The integration of molecular docking and ADMET analysis data led to the identification and prioritization of lead compounds with potent BCL-2 inhibitory activity and favourable pharmacokinetic profiles. This research contributes to the on-going efforts in drug discovery, emphasizing the potential of 1,3,4-oxadiazoles as a class of compounds with significant anticancer properties through BCL-2 inhibition.

Integrative computational approaches for discovery and evaluation of lead compound for drug design

In the drug discovery and development, the identification of leadcompoundsplaysa crucial role in the quest for novel therapeutic agents. Leadcompounds are the initial molecules that show promising pharmacological activity againsta specific target and serve as the foundation for drug development. Integrativecomputational approaches have emerged as powerful tools in expediting this complex andresource-intensive process. They enable the efficient screening of vast chemical librariesand the rational design of potential drug candidates, significantly accelerating the drugdiscoverypipeline. This review paper explores the multi-layered landscape of integrative computationalmethodologies employed in lead compound discovery and evaluation. These approaches include various techniques, including molecular modelling, cheminformatics, structure-based drug design (SBDD), high-throughput screening, molecular dynamics simulations, ADMET (absorption, distribution, metabolism, excretion, and toxicity) prediction, anddrug-target interaction analysis. By revealing the critical role ofintegrative computational methods, this review highlights their potential to transformdrug discovery into a more efficient, cost-effective, and target-focused endeavour, ultimately paving the way for the development of innovative therapeutic agents to addressa multitude of medical challenges.

Abstract 4485: Identification of novel lead compounds and repurposing FDA approved drugs as telomerase inhibitors using structure-based drug designing approach and their evaluation using in-vitro and ex-vivo assays

Abstract Introduction: Upregulated telomerase activity is an established hallmark in about 85-90% of cancer malignancies making it an attractive therapeutic target. However, the development of approved small-molecule telomerase inhibitors has been impeded by a lack of structural ambiguity for decades. In our study, we have employed a molecular dynamics approach to validate the binding site of BIBR1532, an approved telomerase inhibitor in the hTERT thumb domain followed by a structure-based drug designing approach to repurpose FDA-approved drugs and identify novel lead compounds targeting the enzyme. Material and Methods: Due to the unavailability of a co-crystalized structure of BIBR1532 with the catalytic hTERT thumb domain, we employed a Molecular dynamics-based method to identify and validate the exact binding site of the inhibitor. Two pharmacophore models were generated for the putative and newly identified binding pockets which were screened virtually through an anti-cancer library and Drug Bank database. The models were validated on the basis of the fit value of the BIBR1532 and selectivity value indicating the favorable feature set required. The top hits obtained were filtered using drug-likeliness parameters like Lipinski, ADME, and TOPKAT followed by redocking into their binding site. Finally, lead molecules that were able to dock in the new pocket were validated using Molecular Dynamics (MD)simulation studies and their binding free energy was calculated using MM-PBSA calculations followed by their evaluation using in-vitro TRAP assay. Results: Structural investigation, molecular docking studies, and confirmatory molecular dynamics revealed that the exact binding site of BIBR1532 is 8.4 Å away from the reported FVYL pocket strikingly with clinically relevant and characteristic interactions conserved. We have identified five lead compounds and four lead compounds from DrugBank and the anti-cancer library respectively, that were stable in the new binding pocket based on their MD trajectory analysis, and parameters like RMSD, RMSF, H-bonds and Radius of Gyration as well as their MM-PBSA scores. The lead compounds are being evaluated in vitro using TRAP assay and ex-vivo assays. Conclusion: The study identified five existing FDA-approved drugs in the market for other diseases as potential telomerase inhibitors alongside four novel compounds that can be used for the treatment of cancer in the future after further validation studies. Citation Format: Divpreet Kaur, Madhu Chopra, Daman Saluja. Identification of novel lead compounds and repurposing FDA approved drugs as telomerase inhibitors using structure-based drug designing approach and their evaluation using in-vitro and ex-vivo assays [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 4485.