1.in vivo model to study drug molecule properties including absorption, distribution, metabolism and excretion.
2.Quantitative studies on the pharmacological properties of drug molecules via LC/MS.
3.Leads pre-clinical PK/PD evaluation of candidate and development molecules in disease relevant models.
4.Leads the evaluation and implementation of new technologies and laboratory techniques to improve quality and robustness of sample analysis.
5.Maintains compliant laboratory records, consistently and accurately documents all laboratory activities in laboratory notebooks and internal technical reports.

1.Ph.D. or MS after 5 years of graduation in pharmacology, Immunology, Cell Biology, Molecular Biology or related field with 5+ years or M.S. with 10+ years of relevant experience.
2.Ability to plan and carry out experiments independently with strict adherence to laboratory methods and SOPs.
3.Experience with managing in vivo studies from study design to resourcing sample analysis.
4.Demonstrates proficient written and oral communication and presentation skills and a documented ability to troubleshoot technical issues.
5.Desire to work in a dynamic, fast-paced team environment and to make a difference in the lives of people through our innovative medicines.

1. Effectively managing multiple projects through the drug discovery life cycle that includes target validation, lead identification, lead optimization, intellectual property protection, candidate selection and pre-clinical development activities.
2. Maintaining integrity and accuracy of project information to meet team, management and portfolio requirements.
3. Responsible for providing considered and focused input across all phases of drug discovery, smoothly collaborates with other functions in drug discovery, and exemplifies leadership, demanding excellence in performance at all levels.
4. Maintain a dynamic interaction with external CRO resources to ensure synthetic and data needs are fulfilled according to agreed timelines and standards.

1. M.S. (10-15 years) or PhD (8-10 years) in Chemistry, with extensive drug discovery experience in industry.
2. The successful candidate must have project management experience on cross-discipline drug development teams and proven leadership and management skills, with an in-depth understanding of project management best practice.
3. The project manager is required to possess excellent interpersonal skills, including the ability to clearly and succinctly communicate in a timely manner. He or she will be expected to demonstrate a proven ability to foster important relationships with collaborators and business development team to interact effectively with senior management in public forums and within project teams CRM experience is a plus.

1. In charge of establishing and managing CADD platform.
2. Guide new drug design and advance drug discovery project.
3. Working with different experimental groups. Update progress to project leaders.
4. Design molecules and compound libraries using a broad spectrum of computational tools, in collaboration with a team of medicinal chemists.
5. Use advanced computational techniques, including the latest physics-based and AI/ML-based methods, on Linux-based computers internally and in the cloud to drive the discovery of new medicines.
6. Collaborate within AlphaMol and with external academic and industrial partners to develop new computational technologies to address key questions in drug discovery.

1. A PhD or a MS after 5 years of graduation in Computational Chemistry or related field is required.
2. A minimum of 3 years’ experience in the (bio)pharmaceutical industry is required.
3. Experience working with multi-disciplinary/cross-functional teams is required.
4. Familiarity with one or more standard academic or commercial computational chemistry packages (examples might include: Maestro/Schrodinger, MOE/Chemical Computing Group, OpenEye) is required.
5. A strong scientific track record of publications in peer-reviewed journals and/or patent literature is required.
6. Experience with one or more of the following is preferred: Scripting workflows and implementing new algorithms. Applying cheminformatics toolkits and packages (e.g., Pipeline Pilot, KNIME, or ChemAxon) to analyze large scale data sets from multiple sources.
7. Conducting high-throughput virtual screening. Applying ML/AI techniques to drug discovery.