Dr. Jun Mao | Psychopharmacology | Best Researcher Award
Dr. Jun Mao at Sichuan University | China
Dr. Jun Mao is a distinguished researcher in the field of biomedicine, pharmacology, and computational drug design. With a strong academic foundation in genetic and cell engineering, cheminformatics, and bioinformatics, he has dedicated his career to advancing innovative methodologies for drug discovery and psychological health. His work integrates cutting-edge techniques such as molecular modeling, machine learning, and deep learning to address complex biomedical challenges. Dr. Mao’s research has led to impactful contributions in developing prediction models, web-based tools, and novel therapeutic candidates, bridging the gap between computation and clinical application. His publications are recognized in high-impact scientific journals.
Publication Profile
Orcid
Education
Dr. Jun Mao pursued his education at renowned Chinese institutions, building expertise in both experimental and computational biomedical sciences. He studied Genetic and Cell Engineering at Chengdu Medical College, where he developed a strong foundation in biotechnology and molecular biology. At Northwest Normal University, he advanced into Drug Design and Molecular Modelling, deepening his knowledge in structural biology and computational chemistry. His academic journey continued at Sichuan University, specializing in Cheminformatics and Bioinformatics, where he honed skills in data-driven medical research and computational approaches to drug discovery. This multidisciplinary training uniquely equipped him to explore challenges in medicine and pharmacology.
Experience
Dr. Jun Mao has accumulated extensive experience across the intersection of computational science, pharmacology, and biomedical innovation. His professional endeavors focus on utilizing molecular modeling and deep learning to identify therapeutic targets and develop novel drugs. He has contributed to creating medical and psychological databases, designing web-based platforms, and implementing machine learning models for predictive medicine. His research includes identifying new scaffolds for microtubule stabilizers, DNA topoisomerase inhibitors, and PDE inhibitors. Beyond pharmacology, he has also developed computational tools to assess toxicity and neuropsychological disorders, demonstrating his versatility. His cross-disciplinary expertise has significantly advanced predictive modeling and precision medicine.
Awards and Honors
Dr. Jun Mao has earned recognition for his outstanding contributions to biomedicine, computational pharmacology, and machine learning applications in healthcare. His research publications in internationally acclaimed journals, including Nature Communications and Chemico-Biological Interactions, highlight the novelty and global impact of his work. He has been honored with academic awards for innovation in computational drug design, and his contributions have received acclaim for bridging computation with clinical translational applications. His development of interpretable prediction models, web tools, and virtual screening platforms has established him as a thought leader in computational pharmacology, earning invitations to collaborative research projects and academic partnerships.
Research Focus
Dr. Jun Mao research focuses on the application of computational intelligence to medicine and pharmacology. He integrates machine learning, deep learning, and molecular modeling to identify new drug targets, study molecular mechanisms, and develop novel therapeutic agents. His work explores psychoinformatics to predict mental health disorders, assisting clinical applications through interpretable computational models. He develops specialized web-based tools, databases, and prediction frameworks to enhance drug safety and toxicity assessments. His efforts extend to anticancer drug discovery, neurotoxicity evaluation, and stem cell proliferation studies. By uniting computation with biomedical sciences, his research drives innovation in precision and predictive medicine.
Publication Top Notes
Sense-of-Agency as Clinically Accessible Features for Schizophrenia Prediction: Interpretable Ensemble Machine Learning Research and Webserver Development
Identification of low-toxicity DNA topoisomerase I inhibitors with potential blood-brain barrier penetrability for glioblastoma therapy: structure-based virtual screening reveals promising novel Scaffolds.
Identification of Salvianolic acid A as a potent inhibitor of PDEs to enhance proliferation of human neural stem cells
Integrative residue-intuitive machine learning and MD Approach to Unveil Allosteric Site and Mechanism for β2AR
Exploring a general convolutional neural network-based prediction model for critical casting diameter of metallic glasses
Discovery of microtubule stabilizers with novel scaffold structures based on virtual screening, biological evaluation, and molecular dynamics simulation
Discovery of novel microtubule stabilizers targeting taxane binding site by applying molecular docking, molecular dynamics simulation, and anticancer activity testing
Wangzaozin A, a potent novel microtubule stabilizer, targets both the taxane and laulimalide sites on β-tubulin through molecular dynamics simulations
Discovery of novel tubulin inhibitors targeting taxanes site by virtual screening, molecular dynamic simulation, and biological evaluation
Developing novel computational prediction models for assessing chemical-induced neurotoxicity using naïve Bayes classifier technique
Developing novel in silico prediction models for assessing chemical reproductive toxicity using the naïve Bayes classifier method
Development of novel in silico prediction model for drug-induced ototoxicity by using naïve Bayes classifier approach
Conclusion
Dr. Jun Mao demonstrates the hallmarks of a Best Researcher Award recipient through his exceptional contributions to biomedicine, psychoinformatics, machine learning-driven pharmacology, and computational drug discovery. His innovative approach to bridging data science with psychological and medical applications reflects not only deep scientific expertise but also a vision for transforming healthcare. While expanding collaborative efforts and translational research pathways could further amplify his influence, his body of work already reflects an outstanding and future-focused research trajectory. Dr. Mao’s achievements make him a highly suitable and deserving candidate for the Best Researcher Award.