**Job Responsibilities:**
Oversee the comprehensive coordination of computational and informatics aspects within the company's innovative drug research and development initiatives. Lead the AIDD/CADD/computational biology/AI team to leverage existing computational-aided drug design optimization techniques, while exploring cutting-edge methodologies in machine learning, deep learning, and artificial intelligence to expedite the drug development process. This includes target discovery and screening, de novo discovery and screening of large and small molecules, lead compound optimization, candidate molecule submission, and continuous improvement of the AI-driven drug design platform through data integration and analysis. Ultimately, drive projects toward clinical-stage readiness.
Provide strategic support for multiple new drug research and development projects by fostering close collaboration with departments such as chemistry and biology. Develop and implement optimized computational simulation strategies for both large and small molecule drug discovery and development teams.
Manage the collection of drug research and development data and oversee the construction of robust knowledge bases to ensure high-quality, actionable data for informed decision-making in research and development.
Establish standardized processes and protocols for data collection, ensuring compliance and supervising their implementation.
Monitor advancements and emerging trends in computational chemistry and computer-aided drug design, contributing insights to the innovation of drug discovery methodologies.
Collaborate closely with external CROs and academic-industrial partnerships to co-develop feasible projects and accelerate the drug development pipeline.
**Job Requirements:**
Ph.D. in computational chemistry, computational biology, structural biology, biochemistry, or a related field.
Minimum of 8 years of relevant professional experience; candidates with expertise in algorithms, computer programming, and artificial intelligence will be given preference.
Proficiency in molecular modeling and data mining software, along with practical applications in drug discovery workflows.
In-depth understanding of structural biology, protein dynamics, and protein-ligand interactions.
Expertise in structure-based and ligand-based drug design methodologies, including molecular docking, virtual screening, QSAR, pharmacophore modeling, ADMET prediction, molecular dynamics, and binding free energy calculations.
Demonstrated scientific rigor, meticulous attention to detail, strong leadership, and exceptional communication skills.
Superior logical reasoning abilities and fluency in English reading and writing.