Computational Chemist

About Proxima

Proxima (formerly VantAI) is advancing an AI-native approach to drug discovery by making protein interactions programmable. Our platform brings together foundation-model machine learning, a scalable data generation engine, and a partnership track record exceeding $5B in collaborations across the world’s leading biopharma and tech organizations. We’ve recently closed an oversubscribed seed round partnering us with an elite group of sophisticated and dedicated VCs including DCVC, Nvidia’s Nventures, AIX, Yosemite among others.

Neo-1 is our all-atom foundation model that combines state-of-the-art structure prediction and molecular generation in a single system. Neo-1 enables rapid exploration of chemical and structural space for high value, previously intractable targets, and in particular unlocks small molecule proximity therapeutics like molecular glues with AI for the first time.

In parallel, we are developing an advanced structural interactomics platform built on proprietary XLMS technology and a lab equipped with next-generation mass spectrometry instrumentation. This platform produces proteome-scale maps of protein interactions and helps identify small molecules that modulate proximity. Together with Neo-1, it creates an integrated system capable of co-folding protein complexes while generating candidate small molecules to influence those interactions.

Proximity-based therapeutics represent one of most promising frontiers in modern drug discovery with the potential to treat previously intractable diseases and target ‘undruggable’ proteins. Our technology combines proteome-scale structural data with state-of-the-art generative AI foundation models, and coupled with our talented team of scientists and engineers we are uniquely well-positioned to discover and develop a new class of medicines. Come join us!

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