Vant
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.
#J-18808-Ljbffr
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.
#J-18808-Ljbffr