Generative AI for Designing Molecules with Tailored Properties

About the Project

This project leverages advanced Generative AI (Gen-AI) models to accelerate the discovery of novel molecules with tailored physical properties. Cutting-edge tools, including graph neural networks (GNNs), variational autoencoders (VAEs), and transformer-based architectures (e.g., ChemBERTa, MolGPT), are utilised to design molecules optimised for properties such as blue light emitting.

Generative AI methodologies, such as conditional generation and multi-objective optimisation, are combined with high-fidelity quantum mechanical simulations to identify and refine candidate molecules. The approach overcomes the computational limitations and subjective biases inherent in traditional molecular design by enabling efficient exploration of vast chemical spaces. The models are trained on curated, property-labelled datasets to ensure relevance and applicability.

Expected Outcomes:

1. Novel Molecular Structures: Discovery of diverse molecular candidates with optimised physical properties, validated through rigorous computational simulations.
2. Generative AI Workflow: Development of a robust, scalable AI pipeline tailored for molecular discovery, integrating cutting-edge model architectures (e.g., VAEs, GNNs, transformers) with predictive property modules.
3. Validated Predictive Models: Creation of high-accuracy property-prediction models benchmarked against established datasets and validated with quantum mechanical methods.

Keywords: generative AI, graph neural networks, variational autoencoders, transformers, quantum mechanical simulations, multi-objective optimisation.

Requirements:

Essential:

• Bachelor's or Master's degree (2:1 or above) in relevant fields such as Computer Science, Chemistry, Physics, Mathematics or a related discipline.
• Strong communication skills.
• Understanding of research methodologies.
• Proficiency in programming languages such as Python, TensorFlow, or PyTorch.
• Familiarity with machine learning and deep learning techniques.

Desirable:

• Prior experience in Generative AI research or materials design and discovery.
• Ability to work independently and collaboratively in an interdisciplinary environment.
• Creative problem-solving skills and a passion for exploring the intersection of AI, and chemistry.

How to Apply:

Interested candidates should email Dr Yashar Moshfeghi (yashar.moshfeghi@strath.ac.uk) and/or Dr Tahereh Nematiaram (tahereh.nematiaram@strath.ac.uk) and include the following attachments:

• Cover letter detailing contact information, motivation, background, and proposed research direction (max 3 pages).
• Up-to-date CV.
• Transcripts and certificates of all degrees.
• Two references, one academic.

Applications will be processed on a 'first come, first served' basis, and the hiring process will conclude as soon as a suitable candidate is identified. We are committed to inclusion across race, gender, age, religion, identity, and experience, and we believe that diversity makes us stronger by bringing in new ideas and perspectives. The University of Strathclyde was established in 1796 as “the place of useful learning”. This remains at the forefront of our vision today for Strathclyde to be a leading international technological university that makes a positive difference in the lives of its students, society and the world. Strathclyde was the first institute to win the coveted Times Higher Education “University of the Year” award twice, in 2012 and 2019, and has since been voted the Scottish University of the Year in 2020.