- Artificial intelligence-based screening of molecules from traditional medicines
- Aim: Screening molecules for potential therapeutic properties
- Area: Chemistry/Drug Discovery
- Theoretical Knowledge: Machine learning algorithms, Cheminformatics tools, Organic chemistry
- Software/Tools: Machine learning algorithms
- Virtual screening of NIH 10000 molecules for discovering anti-TB drug leads
- Aim: Identifying potential anti-TB compounds from a large dataset
- Area: Chemistry/Drug Discovery
- Theoretical Knowledge: Virtual screening software, Machine learning algorithms, Chemistry, Cheminformatics, Machine learning
- Software/Tools: Virtual screening software (e.g., AutoDock, Vina), Machine learning algorithms
- Repurposing of FDA approved drugs against tuberculosis targets
- Aim: Repurposing existing drugs (FDA) for tuberculosis treatment
- Area: Chemistry/Drug Repurposing
- Theoretical Knowledge: Cheminformatics tools, Machine learning algorithms, Chemistry, Cheminformatics, Machine learning
- Software/Tools: Cheminformatics tools, Machine learning algorithms
- Molecular dynamics simulation of anti-TB molecules
- Aim: Understanding the dynamic behavior of anti-TB compounds at the molecular level
- Area: Chemistry/Physics/Molecular Dynamics Simulation
- Theoretical Knowledge: Molecular dynamics software, Visualization tools, Computational chemistry, Molecular dynamics, Physics
- Software/Tools: Molecular dynamics software (e.g., GROMACS, AMBER), Visualization tools
- Machine learning-based prioritization of organic molecules
- Aim: Prioritizing molecules for further experimental validation
- Area: Chemistry/Drug Discovery
- Theoretical Knowledge: Machine learning algorithms
- Software/Tools: Machine learning algorithms
- Prediction of electronic properties of molecules
- Aim: Predicting electronic properties (e.g., charge, energy levels) of molecules
- Area: Computational Chemistry
- Theoretical Knowledge: Quantum chemistry software, Machine learning tools, Quantum chemistry
- Software/Tools: Quantum chemistry software (e.g., Gaussian, GAMESS), Machine learning tools
- Development of innate immunity model for preclinical prioritization of drug-like organic molecules
- Aim: Modeling innate immunity to prioritize drug candidates
- Area: Chemistry/Drug Discovery
- Theoretical Knowledge: Machine learning algorithms, Immunology models, Immunology, Drug discovery
- Software/Tools: Machine learning algorithms
- Spectroscopic analysis of FDA approved drug
- Aim: Analyzing the spectroscopic properties of FDA-approved drugs
- Area: Analytical Chemistry
- Theoretical Knowledge: Spectroscopy instruments, Data analysis software, Spectroscopy, Analytical chemistry
- Software/Tools: Spectroscopy instruments, Data analysis software
- Prioritization anti-viral molecules based on quantum chemical descriptors
- Aim: Prioritizing anti-viral compounds based on their quantum chemical properties
- Area: Chemistry/Drug Discovery
- Theoretical Knowledge: Quantum chemistry software, Machine learning algorithms, Quantum chemistry, Machine learning
- Software/Tools: Quantum chemistry software (e.g., Gaussian), Machine learning algorithms
- Deep learning-based prediction of drug-protein interactions for tuberculosis targets
- Aim: Predicting interactions between drugs and tuberculosis proteins
- Area: Chemistry/Drug Discovery
- Theoretical Knowledge: Deep learning frameworks, Bioinformatics tools, AI/Bioinformatics, Machine learning
- Software/Tools: Deep learning frameworks (e.g., TensorFlow, PyTorch), Bioinformatics tools
- Virtual screening of natural compounds from medicinal plants for anti-tuberculosis activity
- Aim: Screening natural compounds for potential anti-TB activity
- Area: Chemistry/Drug Discovery
- Theoretical Knowledge: Virtual screening software, Machine learning algorithms, Molecular biology, Cheminformatics, Machine learning
- Software/Tools: Virtual screening software, Machine learning algorithms
- Molecular docking studies of FDA-approved drugs against tuberculosis targets
- Aim: Studying the binding of FDA-approved drugs to tuberculosis targets
- Area: Chemistry/Drug Discovery
- Theoretical Knowledge: Molecular docking software, Molecular dynamics tools, Molecular biology, Computational chemistry, Molecular dynamics
- Software/Tools: Molecular docking software (e.g., AutoDock, GOLD), Molecular dynamics tools
- Design and implementation of a machine learning model for predicting drug resistance in tuberculosis
- Aim: Predicting drug resistance in tuberculosis using machine learning techniques
- Area: Chemistry/Drug Resistance
- Theoretical Knowledge: Machine learning algorithms
- Software/Tools: Machine learning algorithms
- Deep learning-based prediction of pharmacokinetic properties of potential anti-TB drugs
- Aim: Predicting pharmacokinetic properties of potential anti-TB drugs
- Area: Chemistry/Drug Discovery
- Theoretical Knowledge: Deep learning frameworks, Pharmacokinetic modeling tools
- Software/Tools: Deep learning frameworks, Pharmacokinetic modeling tools
- Molecular dynamics simulation of drug delivery systems for tuberculosis treatment
- Aim: Simulating drug delivery systems to understand their behavior in tuberculosis treatment
- Area: Drug Delivery
- Theoretical Knowledge: Molecular dynamics software, Visualization tools, Computational chemistry, Drug delivery
- Software/Tools: Molecular dynamics software, Visualization tools
- Predicting the potential toxicity of anti-TB drugs
- Aim: Predicting the toxicity of anti-TB drugs to enhance safety profiles
- Area: Chemistry/Drug Discovery/Toxicology
- Theoretical Knowledge: Machine learning algorithms
- Software/Tools: Machine learning algorithms
- Integrating AI algorithms for predicting drug synergies in tuberculosis combination therapy
- Aim: Predicting synergistic effects of drug combinations for tuberculosis treatment
- Area: Combination Therapy
- Theoretical Knowledge: Machine learning algorithms
- Software/Tools: Machine learning algorithms
- Virtual screening of chemical libraries for novel anti-tuberculosis compounds using AI-driven methodologies
- Aim: Screening chemical libraries for potential anti-TB compounds using AI-driven methodologies
- Area: Chemistry/Drug Discovery
- Theoretical Knowledge: Virtual screening software, Machine learning algorithms, Molecular biology, Cheminformatics, Machine learning
- Software/Tools: Virtual screening software, Machine learning algorithms
- Deep learning-based analysis of high-throughput screening data for identifying promising hits against TB
- Aim: Analyzing high-throughput screening data to identify potential anti-TB compounds
- Area: Chemistry/Drug Discovery
- Theoretical Knowledge: Deep learning frameworks, High-throughput screening data analysis tools, Bioinformatics, Machine learning
- Software/Tools: Deep learning frameworks, High-throughput screening data analysis tools
- Utilizing AI techniques to analyze omics data for understanding host-pathogen interactions in TB
- Aim: Analyzing omics data to understand host-pathogen interactions in tuberculosis infection
- Area: Artificial Intelligence/Chemistry/Drug Discovery
- Theoretical Knowledge: Bioinformatics tools, Machine learning algorithms, Bioinformatics, Immunology
- Software/Tools: Bioinformatics tools, Machine learning algorithms
- Deep learning-based prediction of drug-induced liver injury for potential anti-tuberculosis compounds
- Aim: Predicting drug-induced liver injury for potential anti-TB compounds
- Area: Drug Safety
- Theoretical Knowledge: Deep learning frameworks, Toxicology data, Pharmacology, Toxicology
- Software/Tools: Deep learning frameworks, Toxicology data
- Virtual screening of natural product databases for compounds targeting tuberculosis persistence
- Aim: Screening natural product databases for compounds targeting persistent tuberculosis infections
- Area: Medicinal Chemistry/Drug Discovery
- Theoretical Knowledge: Virtual screening software, Machine learning algorithms, Molecular biology, Cheminformatics, Machine learning
- Software/Tools: Virtual screening software, Machine learning algorithms
- Machine learning-driven identification of novel drug combinations for tuberculosis treatment
- Aim: Identifying novel drug combinations for tuberculosis treatment based on synergy prediction
- Area: Combination Therapy
- Theoretical Knowledge: Machine learning algorithms
- Software/Tools: Machine learning algorithms
- Predicting off-target effects of anti-tuberculosis drugs using AI techniques to enhance safety profiles
- Aim: Predicting off-target effects of anti-TB drugs to improve drug safety profiles
- Area: Drug Safety
- Theoretical Knowledge: Machine learning algorithms
- Software/Tools: Machine learning algorithms
- Development of a deep learning model for predicting the binding affinity of small molecules to tuberculosis protein targets
- Aim: Predicting the binding affinity of small molecules to tuberculosis protein targets using deep learning
- Area: Drug Discovery
- Theoretical Knowledge: Deep learning frameworks, Bioinformatics tools, Bioinformatics, Pharmacology
- Software/Tools: Deep learning frameworks, Bioinformatics tools
- Virtual screening of compound databases for repurposing candidates targeting tuberculosis biofilms guided by AI
- Aim: Screening compound databases for repurposing candidates targeting tuberculosis biofilms using AI
- Area: Drug Repurposing
- Theoretical Knowledge: Virtual screening software, Machine learning algorithms, Molecular biology, Cheminformatics, Machine learning
- Software/Tools: Virtual screening software, Machine learning algorithms
- Utilizing generative adversarial networks (GANs) for de novo design of anti-tuberculosis compounds with desired properties
- Aim: Designing anti-TB compounds with desired properties using GANs
- Area: Computer Science/Drug Discovery
- Theoretical Knowledge: Generative adversarial networks (GANs), Computational chemistry
- Software/Tools: Computational chemistry, Drug design
- Machine learning-based prediction of drug metabolism pathways for anti-tuberculosis agents to aid in optimization
- Aim: Predicting drug metabolism pathways for anti-TB agents to optimize drug design
- Area: Pharmacokinetics
- Theoretical Knowledge: Machine learning algorithms, Pharmacokinetic modeling tools, Pharmacokinetics, Machine learning
- Software/Tools: Machine learning algorithms, Pharmacokinetic modeling tools
- Deep learning-driven prediction of drug permeability across the blood-brain barrier for potential tuberculosis treatments
- Aim: Predicting drug permeability across the blood-brain barrier for potential TB treatments
- Area: Drug Delivery
- Theoretical Knowledge: Deep learning frameworks, Pharmacokinetic modeling tools, Pharmacokinetics, Drug delivery
- Software/Tools: Deep learning frameworks, Pharmacokinetic modeling tools
- Integrating AI algorithms for identifying potential drug resistance mechanisms in tuberculosis bacteria from genomic data
- Aim: Identifying potential drug resistance mechanisms in TB bacteria from genomic data
- Area: Drug Resistance
- Theoretical Knowledge: Machine learning algorithms, Genomic analysis tools, Pharmacology, Genomics
- Software/Tools: Machine learning algorithms, Genomic analysis tools
- Computational study of HOMO and LUMO energies in organic semiconductors
- Aim: Analyzing the electronic properties of organic semiconductors using HOMO and LUMO energies
- Area: Materials Science, Electronic Structure Calculation
- Theoretical Knowledge: Quantum chemistry software, Visualization tools, Computational chemistry, Semiconductor physics
- Software/Tools: Quantum chemistry software, Visualization tools
- Machine learning-based prediction of organic semiconductor properties
- Aim: Predicting properties (e.g., charge transport, optical properties) of organic semiconductors
- Area: Materials Science, Machine Learning
- Theoretical Knowledge: Machine learning algorithms
- Software/Tools: Machine learning algorithms
- Deep learning-driven design of novel organic semiconductors with optimized electronic properties
- Aim: Designing organic semiconductors with desired electronic properties using deep learning techniques
- Area: Chemistry/AI/Materials Science, Deep Learning
- Theoretical Knowledge: Deep learning frameworks, Computational chemistry tools, Materials science, Computational chemistry, Machine learning
- Software/Tools: Deep learning frameworks, Computational chemistry tools
- Molecular dynamics simulation of organic semiconductor materials
- Aim: Understanding the dynamic behavior of organic semiconductor materials at the molecular level
- Area: Materials Science, Molecular Dynamics Simulation
- Theoretical Knowledge: Molecular dynamics software, Visualization tools, Computational chemistry, Materials science, Molecular dynamics
- Software/Tools: Molecular dynamics software, Visualization tools
- Prediction of antibacterial/antiviral activity using quantitative structure-activity relationship (QSAR)
- Aim: Predicting the activity of antibacterial/antiviral molecules based on their chemical structures
- Area: Chemistry/Drug Discovery, Cheminformatics
- Theoretical Knowledge: QSAR modeling software, Machine learning algorithms, Pharmacology, Cheminformatics, Machine learning
- Software/Tools: QSAR modeling software, Machine learning algorithms
- Virtual screening of compound libraries for novel antibacterial agents using QSAR
- Aim: Screening compound libraries for potential antibacterial agents using QSAR modeling
- Area: Drug Discovery, Cheminformatics
- Theoretical Knowledge: Virtual screening software, QSAR modeling tools, Cheminformatics, Pharmacology, Drug discovery
- Software/Tools: Virtual screening software, QSAR modeling tools
- Molecular docking studies of potential antiviral compounds against viral targets
- Aim: Studying the binding interactions between potential antiviral compounds and viral protein targets
- Area: Drug Discovery, Molecular Docking
- Theoretical Knowledge: Molecular docking software, Molecular dynamics tools, Molecular biology, Computational chemistry, Drug discovery
- Software/Tools: Molecular docking software, Molecular dynamics tools
- Machine learning-driven prediction of drug resistance mechanisms in bacteria and viruses
- Aim: Predicting drug resistance mechanisms in bacteria and viruses using machine learning techniques
- Area: Drug Resistance, Machine Learning
- Theoretical Knowledge: Machine learning algorithms, Pharmacology, Microbiology, Machine learning
- Software/Tools: Machine learning algorithms
- Computational analysis of pharmacophore features in antibacterial/antiviral molecules
- Aim: Identifying key pharmacophore features in antibacterial/antiviral molecules
- Area: Drug Discovery, Cheminformatics
- Theoretical Knowledge: Molecular modeling software, Cheminformatics tools, Pharmacology, Cheminformatics
- Software/Tools: Molecular modeling software, Cheminformatics tools
- Virtual screening of natural product databases for novel antiviral compounds
- Aim: Screening natural product databases for potential antiviral compounds
- Area: Drug Discovery, Cheminformatics
- Theoretical Knowledge: Virtual screening software, Cheminformatics tools
- Software/Tools: Virtual screening software, Cheminformatics tools