Guest Editors
Dr. Nitin Mittal, Chandigarh University, India.
Dr. Preeti Bajaj, Galgotias University, India.
Prof. Meenakshi Sharma, Galgotias University, India.
Dr. Gaurav Kumar, Galgotias University, India.
Dr. Oana Geman, "Stefan cel Mare” University Suceava, Romania.
Summary
Computational pharmacology and therapeutics is an emerging area and attracting the attention of researchers working in the field of computational chemistry, drug discovery, drug development and disease diagnosis. Computational pharmacologists aim to develop the techniques that can be used to integrate and capture the medical and biological data, simulate the physiological conditions, determine the biological activity of new drugs. The diagnosis and prognosis of neurological disorders, cancer, cardiovascular disorders, respiratory disorders and gastrointestinal diseases is vital for the treatment and patients’ wellbeing. The interpretation of diagnostic data is challenging, complex and error prone Artificial intelligence and machine learning applications are decisive for the improvement of disease diagnosis efficiency and accuracy. The artificial intelligence is widely utilized for the drug discovery and development, personalized medicine, and gene editing.
This special issue welcomes original research and review papers focused on the computational chemistry, computational therapeutics and computational pharmacology including Molecular Dynamic Simulation, Molecular Docking, Structure based Drug designing, Virtual Screening, Application of artificial intelligence or machine learning in disease diagnosis and therapy.
Keywords
1. Structure based Drug Discovery
2. Artificial Intelligence in targeted Drug Delivery
3. Artificial Intelligence for cancer diagnosis and prognosis
4. Neuroprotective strategies using computational pharmacology
5. AI for Microfluidic devices
6. AI & ML for personalized medicine
7. Development of servers for small molecular drug likeness prediction
8. Software for disease diagnosis and patient monitoring
9. drug–drug interactions using AI & ML
10. Protein-Protein interaction using AI
11. Protein-Drug interaction predictor
12. ADME Toxicity Prediction using molecular structure
13. AI & ML for Clinical Trials
14. AI for pharmacovigilance
15. AI & ML for Disease etiology
16 Artificial Intelligence for Gene Editing
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