Deep Learning Guided Designing Of Pks13 Inhibitors

Pharmacophore Guided Deep Learning Approach to Identify Novel Inhibitors Targeting Mycobacterial Polyketide Synthase Pks13-TE Domain

Tuberculosis (TB), an enduring global health challenge, persists due to the rise of drug-resistant Mycobacterium tuberculosis (MTB) strains. Among potential therapeutic targets, Pks13, a protein crucial for mycolic acid biosynthesis, is key for Mtb’s virulence and survival. This study employed a pharmacophore-based drug design approach, employing the Pharmacophore-Guided Molecular Generation (PGMG) tool to target Pks13 inhibitors. Aromatic, hydrophobic, positive ion and hydrogen bond acceptors pharmacophoric features were identified from co-crystal ligands. Candidate compounds underwent evaluation of pharmacokinetic properties with the ADMET_AI tool. Further refinement involved molecular docking with PLANTS software, absolute binding free energy calculations via KDeep, and toxicity assessments using eToxPred. MM-GBSA, PCA, DCCM, and FEL were incorporated to validate and refine inhibitors accurately. From this analysis, we discovered five novel hit molecules. We conclude that the screened hit compounds may act as potential inhibitors targeting Pks13 and further preclinical and clinical studies may pave the way for developing them as effective therapeutic agents for the treatment of MTB. The article has been published in the Journal of Molecular Structure, and it can be available at Science Direct.

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In-Silico Studies for Kinase PknB Peptide Design

Identification And Design Of Novel Antimicrobial Peptides Targeting Mycobacterial Protein Kinase Pkn

Antimicrobial peptides (AMPs) are increasingly favored over small molecule inhibitors due to their multifunctionality, ease of synthesis, and target specificity. Our recent study aimed to identify an AMP that inhibits PknB of M. tuberculosis, a serine/threonine protein kinase, by binding at its hinge region. A library of 5626 AMPs was prepared and categorized by length, and molecular docking identified peptides with consistent high-affinity binding to PknB’s active site. Twenty-six peptides were shortlisted and further grouped by length, with five chosen for molecular dynamic simulations in Gromacs based on binding affinity. Post-simulation analysis identified a 15-mer peptide as the most effective, leading to residue substitution to enhance interactions. Elastic Network Model (ENM) analysis further evaluated emphasizing the importance of peptide length and residue composition in developing effective peptide-based inhibitors. Article “Identification and Design of Novel Antimicrobial Peptides Targeting Mycobacterial Protein Kinase Pkn” published in The Protein Journal.

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AI/ML Approaches For Pks13 Inhibitors Discovery

Identification of novel hit molecules targeting M. tuberculosis polyketide synthase 13 by combining generative AI and physics-based methods

Our latest research gives insight into the critical Pks13-TE domain of mycobacteria, employing a mix of AI and physics-based tools to identify new inhibitors. We utilized AI-ML tools such as Reinvent 4, pKCSM, KDeep, and SwissADME, alongside physics-based methods like AutoDock Vina, PLANTS, MDS, and MM-GBSA. Known Pks13-TE inhibitors were curated and used as input to generate novel inhibitors, which were then filtered through rigorous molecular docking and dynamics simulations. This meticulous process reduced the chemical space, retaining only the most promising compounds based on their interactions, stability, and binding energies. Further ADMET testing of these compounds revealed that ligands Mt1 to Mt6 possess excellent pharmacokinetic, pharmacodynamic, and toxicity profiles, making them strong candidates for drug development. The study “Identification of novel hit molecules targeting M. tuberculosis polyketide synthase 13 by combining generative AI and physics-based methods”, published in Computers in Biology and Medicine at Science Direct, showcases a robust framework for the discovery of potential Pks13-TE inhibitors.

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AI/ML Assisted De Novo Design Of Hsp90 Inhibitors

Machine learning aided de novo design identifies novel Benzimidazolone based inhibitor-modulators for Heat Shock Protein 90 (HSP90)

Our research began with a machine learning-assisted de novo design process, employing REINVENT4 to design and generate novel molecular structures inspired by known HSP90 inhibitors. The generated molecules underwent a series of rigorous cheminformatics analyses to assess their pharmacokinetic properties. We then evaluated their binding affinities through docking simulations and predicted their absolute binding affinity using the KDeep tool, thereby refining the designed chemical space. From our comprehensive analyses, three benzimidazole-based drug-like candidates emerged as promising HSP90 inhibitors: IM1, IM2, and IM3. Their docking-based binding affinities were impressive, registering at -11.30, -11.50, and -11.20 kcal/mol, respectively. Published article “Machine learning aided de novo design identifies novel Benzimidazolone based inhibitor-modulators for Heat Shock Protein 90 (HSP90)” found in Chemistry Select.

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Aurora Kinase Inhibitors Identification: An AI/ML Study

Molecular Simulations and Machine Learning Methods for the Identification of Novel Aurora A Kinase Inhibitors

Aurora kinase A (AAK) is a critical regulator of mitosis, the process of cell division. It plays essential roles in cell cycle regulation, centrosome maturation, spindle assembly, and chromosome segregation, all crucial for accurate daughter cell formation. Deregulation of AAK expression and activity has been linked to various human diseases, particularly cancer, where increased AAK expression or activity contributes to cancer development and progression. In our recent study, we have employed artificial intelligence and machine learning techniques to identify potential inhibitors of AAK. Particularly we used multi-step molecular docking via AutoDock Vina and PLANTS to screen a ChemDiv kinase-specific inhibitor library against AAK. The study successfully identified three hit compounds with perfect binding in the active site pockets of AAK, comparable to the standard BindingDB compound and the co-crystal ligand VX-680 binding mode. These findings underscore the potential of computational drug discovery, bolstered by AI and machine learning, in identifying promising AAK inhibitors for improved cancer management. Published research article “Molecular Simulations and Machine Learning Methods for the Identification of Novel Aurora A Kinase Inhibitors” available in Journal of Biomolecular Structure & Dynamics.

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Identification of Mtb EthR Inhibitors

Identification of Mycobacterium tuberculosis transcriptional repressor EthR inhibitors

Tuberculosis (TB) has plagued humanity since prehistoric times, and the rise of drug-resistant strains has only made it harder to combat. Ethionamide (ETH), a second-line drug, targets mycolic acid synthesis in TB bacteria but requires activation by the EthR protein. Our research has focused on finding new ways to enhance this activation process. In an exciting development, we have identified three promising molecules that could serve as ETH boosters by inhibiting the EthR protein. Advanced techniques like molecular docking, molecular dynamics simulations, and binding free energy studies confirmed the potential of those identified compounds as effective EthR inhibitors. Additionally, machine learning methods evaluated their toxicity and synthesisability, confirming their safety and ease of synthesis. This breakthrough offers new hope in the battle against this ancient disease. Check out this published article “Identification of Mycobacterium Tuberculosis Transcriptional Repressor EthR Inhibitors: Shape-Based Search and Machine Learning Studies” in Heliyon.

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AI/ML Assisted Identification Of InhA Inhibitors

Machine learning assisted methods for the identification of low toxicity inhibitors of Enoyl-Acyl Carrier Protein Reductase (InhA)

Tuberculosis, a deadly disease with ancient roots, continues to pose a global health threat. We have employed artificial intelligence and machine learning integrated approaches to identify potential new treatments by targeting the InhA protein in the tuberculosis-causing bacteria. A large chemical database ‘SelleckChem’ was meticulously filtered using advance computational methods to pinpoint molecules with promising drug-like properties. Molecular dynamics simulations and machine learning based ADMET analyses confirmed the potential of five identified compounds to effectively bind to the InhA protein and disrupt its function. Crucially, these molecules were predicted to be safe and easily producible, offering hope for the development of new anti-tuberculosis drugs. Article “Machine Learning Assisted Methods for the Identification of Low toxicity Inhibitors of Enoyl-Acyl Carrier Protein Reductase (InhA)” can check out in Science Direct.

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