Senior Scientist, Modelling & Informatics

CHARM Therapeutics has a state-of-the-art R&D facility in Cambridge, UK where we are seeking a Senior Computational Scientist with a passion for scientific innovation to join our Modeling and Informatics team. The successful candidate will join a multidisciplinary, highly collaborative discovery team to drive our oncology-focussed drug discovery pipeline and invent novel medicines. Utilising innovative computational chemistry problem-solving skills and leveraging CHARM's 3D-Deep Learning Design platform, he/she will enable the design of new molecules to drive our oncology pipeline. In this role, the incumbent will contribute to the overall scientific deliverables of the research site by helping to define CHARM's computational chemistry strategy, and drive internal programs. Furthermore, he/she will engage with the external scientific community to further our scientific activities and build CHARM's external reputation. To accomplish the above, the successful candidate will have excellent
interpersonal, cross-functional collaboration and communication skills, in addition to rigorous scientific thinking.

What You Will Do:

Apply a breadth of computational chemistry techniques to accelerate the evolution of drug discovery leads to drug candidates in a hypothesis-driven manner

Collaborate with CHARM's ML/AI and data engineering teams to evolve the DragonFold platform and enable further applications to drug discovery programs

Foster collaborations within CHARM and with academia to help ensure that the Modeling and Informatics group operates at the highest levels of scientific excellence

Continue research in areas of computational chemistry relevant to drug discovery and maintain an external scientific presence through authoring significant scientific publications and presentations

PhD or equivalent in computational chemistry, cheminformatics, chemistry or related physical sciences fields

Experience in an industrial/biotech setting is preferred

A strong knowledge of medicinal chemistry and compound design principles, including structure-based drug design and the influence of physicochemical properties

Scripting and programming expertise in languages such as Python

A strong knowledge of the latest innovations and technology in computational chemistry, and an openness to explore new methods for drug discovery

A good understanding of the related scientific disciplines that are pertinent to drug discovery (e.g. chemistry, pharmacology, chemical biology, ADME, structural biology & drug safety/toxicology)

Ability to manage multiple projects simultaneously and flexibly manage priorities

Collaborative, with the ability to work well as part of a multidisciplinary team

The ability and desire to foster academic networks such that CHARM remains an outward looking organisation that can exploit the world's best scientific thinking

Multiple publications and external presentations demonstrating creative application of computational chemistry approaches to problems of biological interest

The ability to work flexibly across the CHARM portfolio

The following skills may be advantageous:

Experience with Linux and high-performance computing

Experience working with cloud- and container-based computing infrastructure such as AWS, Kubernetes, and Docker.

Ability to effectively apply molecular modelling packages such as Openeye Toolkits and RDKit

Understanding and practical application of free energy perturbation, MM-GBSA and other molecular dynamics-based methods

Understanding and practical application of machine learning methods for compound design. Experience constructing and applying QSAR models using statistical machine learning frameworks such as sci-kit learn and deep learning frameworks such as pytorch.

CHARM Therapeutics is a biotech focused on delivering transformational medicines that will address difficult-to-drug targets in key oncogenic pathways. CHARM has developed the first highly accurate, high throughput protein-ligand co-folding algorithm (DragonFold), driven by end-to-end 3D deep learning. Our platform enables the rapid generation of highly differentiated clinical candidates, our ambition is to revolutionise drug discovery.