AI-Driven Computational Chemist

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We seek a talented, energetic, and collaborative AI-driven Computational Chemist to conduct both routine and novel analyses as part of our flagship platform A3D3a: Adaptive, AI-augmented, Drug Discovery and Development. With experience in molecular generation, foundation models, and QSAR/QSPR modeling, the AI Computational Chemist will contribute to the discovery of novel cancer therapeutics as part of A3D3a.

Led by Prof. Bissan Al-Lazikani, Director of Therapeutics Data Science, the intelligent and ever-learning A3D3a platform is part of MD Anderson's Therapeutics Data Science initiative and part of our ambitious Institute for Data Science in Oncology. A3D3a will accelerate the discovery and impact of novel therapies for cancer by enabling novel opportunities for optimized therapies for patients with a focus on rare and hard-to-treat cancers through the development of novel machine learning and AI technologies.

Central to this vision, the AI Computational Chemist will leverage state-of-the-art generative and foundation models and, collaboratively with senior scientists, develop innovative data science approaches to propose chemical matter, propose drug-target interactions of on- and off-target effects, and rationalize toxicity patterns. While physics- or docking-based approaches may complement work in this position, proficiency in AI/ML based computational chemistry and cheminformatics is a must. The preferred candidate should have at least a MSc in Chemistry, Biochemistry, Computer Science, or a related field. Expertise and comfortability with large data and a willingness to learn new approaches is critical to this role.

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The ideal candidate will leverage advanced generative chemistry and AI/ML techniques using frameworks such as PyTorch, predict drug-target interactions, and drive data-informed discovery, while bringing strong computational chemistry expertise, large-scale data fluency, and a collaborative, innovation-focused mindset.

Job Responsibilities

• Apply generative models to propose chemical matter for targets of interest.
• Develop novel methods in coordination with senior scientists.
• Develop QSAR/QSPR models from public and internal data.
• Document work thoroughly.
• Produce output for scientific publications and contribute to said publications.

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• Present results at multidisciplinary project meetings as well as external meetings.
• Attend collaborator meetings and team working group meetings. Prioritize and manage multiple projects in a timely and resource-effective manner.
• Stay up to date with relevant literature, gather information systematically, and confer with the supervisor regarding new procedures.
• Expertise in scientific computing using Python in a Unix environment, plus GPU computing.
• Proficient with generative AI for molecular generation.
• Proficient in PyTorch, rdkit, and other machine/deep learning utilities for cheminformatics.
• Familiar programmatically accessing public databases such as ChEMBL.
• Experience with molecular visualization tools (i.e., PyMol, VMD).
• Familiar with basic statistical testing.
• Other duties as assigned.
  
  

Education

Required: Bachelor's Degree Biomedical Engineering, Electrical Engineering, Computer Engineering, Physics, Applied Mathematics, Science, Engineering, Computer Science, Statistics, Computational Biology, or related field.

Preferred: Master's Degree or PhD in chemistry, structural biology, biochemist, or related field.

Experience

Required: Three years Scientific software or industry development/analysis experience. With master's degree one year experience. With PhD, no experience.

Preferred: Extensive experience with PyTorch. rdKit is a plus.

The University of Texas MD Anderson Cancer Center offers excellent https://www.utsystem.edu/offices/employee-benefits/ut-retirement-program/voluntary-retirement-programs, tuition benefits, educational opportunities, and individual and team recognition.

This position may be responsible for maintaining the security and integrity of critical infrastructure, as defined in Section 113.001(2) of the Texas Business and Commerce Code and therefore may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment.

It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state, or local laws unless such distinction is required by law.http://www.mdanderson.org/about-us/legal-and-policy/legal-statements/eeo-affirmative-action.html

Additional Information

• Requisition ID: 179239
• Employment Status: Full-Time

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• Employee Status: Regular
• Work Week: Days
• Minimum Salary: US Dollar (USD) 106,500
• Midpoint Salary: US Dollar (USD) 133,000
• Maximum Salary : US Dollar (USD) 159,500
• FLSA: exempt and not eligible for overtime pay
• Fund Type: Soft
• Work Location: Hybrid Onsite/Remote
• Pivotal Position: Yes
• Referral Bonus Available?: Yes
• Relocation Assistance Available?: Yes
  
  

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