Computational Chemist

About VantAI:

VantAI is building a computational pipeline combining state-of-the-art physics-based modeling and machine learning to revolutionize drug discovery and development. Working together with some of the world’s leading biopharmaceutical companies, we design, test, and optimize novel therapies to treat some of the world’s most difficult diseases.

Key Responsibilities:

• Lead small-molecule drug discovery projects using internal and external Machine Learning and CADD tools 

• Drive drug discovery programs forward by quickly developing scalable tools to address specific project needs 

• Work independently and in collaboration with Medicinal Chemists to prioritize small-molecule designs, clearly communicating the decisions to interdisciplinary audiences 

• Collaborate with experts from other fields (e.g., Medicinal Chemistry, Machine Learning, Computational and Structural Biology, etc.) to advance integrated in-silico discovery platforms 

• Design and execute large-scale virtual screening campaigns using both ligand and structure-based approaches  

Basic Qualifications:  

• MSc/PhD degree in Chemistry, Computational Chemistry, Biochemistry, Chemical Engineering, or another related subject 

• Minimum 2 years (PhD) or 4 years (MSc) of post-graduate experience in small-molecule drug discovery 

• Proven track-record in advancing drug discovery projects using in-silico methods; strong background in rational drug design 

• Ability to adapt well to a fast-paced environment and get things done by combining creativity, problem-solving skills, and a can-do attitude to overcome obstacles 

• Extensive experience in large-scale virtual screening using structure and ligand-based methods for hit identification and optimization 

• Strong programming skills with at least 2 years of experience using Python for data analysis 

• Excellent written and verbal communication skills along with a strong desire to work in cross-functional teams 

Additional Qualifications (3 or more preferred): 

• Previous experience in chemically induced proximity (molecular glues, PROTACs, etc.), especially in molecular design or in-silico method development 

• Successful track-record in molecular design, working with Medicinal and Synthetic Chemists

• Extensive experience with open-source cheminformatics tools such as RDKit, especially in navigating ultra large-scale chemical spaces via similarity searches, clustering, etc.

• Experience in leveraging experimental data for building and/or refining complex in-silico screening pipelines (e.g. SPR, TSA and cell-based assays readouts, including phenotypic screening) 

• Prior experience in designing chemical screening libraries, including synthesis considerations

• A solid understanding of deep learning-based frameworks applied in structural design (e.g. RoseTTAFold2, DiffDock, DeepDock, GNINA, KDEEP, dMaSIF)

• Experience in developing ML tools to predict protein-ligand poses, binding affinity/ranking or generate target-conditioned small-molecules

• Familiarity with common pitfalls in dataset curation for Machine Learning methods, especially, in the context of small-molecules and proteins

• Familiarity with best software development practices, prior experience in developing Python packages, package management (pip, mamba, conda), CI/CD and related topics necessary for supporting high-quality codebases

• Contribution, development, and maintenance of open-source packages used by the small-molecule discovery community 

NYC Salary: $120,000 - $180,000

This band is a reflection of the job description as written. Looking for a higher salary? Apply anyway! We are happy to speak to more experienced candidates who may require a higher salary and discuss that experience in our first touchpoint.