Alzheimer’s disease (AD) is a chronic, progressive neurodegenerative disorder predominantly affecting 47 million people worldwide. Dysregulation of BACE-1, the initiating enzyme in the amyloidogenic cascade, plays a pivotal role in AD pathogenesis. Conventional anticholinesterase inhibitors can only provide symptomatic relief without stopping disease progression. In this study, we employed an integrated computational approach to rationally design novel indole-based BACE-1 inhibitors through pharmacophore modeling, 3D-QSAR analysis, ligand-protein docking, pharmacophore-guided virtual screening, MD simulation, and ADME studies. DDHRR_1 was identified as the best pharmacophore model with a survival score of 5.7767. Statistically significant 3D-QSAR models were obtained, wherein the atom-based model demonstrated high predictive reliability (R
2
=0.9386, Q
2
=0.7432), while the field-based model exhibited acceptable predictive ability (R
2
=0.8492, Q
2
=0.6644). Among the dataset compounds, compound B38 (3i) displayed the optimal binding affinity (-6.567 kcal/mol; PDB ID: 4DJU), with key interactions at GLY95, TYR132, and PHE169. R-group enumeration generated 2408 derivatives, among which R1 and R2 exhibited strong binding affinity (-10.339 and -9.611 kcal/mol). A comparative analysis of the referenced ligand, the two best-performing ZINC-derived compounds (ZINC43707325 and ZINC03628223), and the two top-ranked R-group compounds revealed that R2 was the major lead compound. However, as these findings are based solely on in-silico analyses, further synthesis, biological evaluation, and BBB permeability studies are necessary to validate the therapeutic potential of R2 against Alzheimer's disease.
Pitam Ghosh, Ryena Dhir, D. Sharma et al.· Journal of Computational Bio...· 0 citations
Drug discovery is a time-consuming and resource-intensive process with a development period of more than ten years and a clinical attrition rate of more than 90%. Despite its contributions to rational drug design, computer-aided drug design has been constrained by limited scalability, overreliance on molecular descriptors, and incomplete modeling of complex biological systems. The emergence of artificial intelligence (AI) has transformed this landscape. AI-based drug discovery platforms have shifted the paradigm from a narrow focus to a comprehensive platform that covers target identification using graph-based drug-target interaction models. Additionally, deep-learning-based docking techniques, such as GNINA and AtomNet, de novo design approaches, such as REINVENT and RANC, and multi-task ADMET predictors, such as ADMETlab 2.0, are all parts of the AI-based drug discovery platform. In addition, AlphaFold has recently predicted more than 200 million protein structures, substantially expanding the pool of accessible drug targets. This review focuses on the AI-based approaches for target discovery, virtual screening, molecular generation, lead optimization, retro-synthesis, and structural modeling while also addressing issues of dataset bias, reproducibility, and real-world applicability. In this review, we discuss the emerging trends of AI-based drug discovery computational tools, which might change the face of medicinal chemistry. This review provides a balanced overview of AI-based drug discovery, including the limitations and challenges, to provide a framework to move this emerging field of science to a more robust, reproducible and clinically applicable platform.
Ryena Dhir, Pitam Ghosh, D. Sharma et al.· RSC Advances· 0 citations
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