Job Description

1. Build homology models for target proteins based on family or species homologs with structural information as a tool to use for docking new compound designs, understanding existing SAR, and identifying potential opportunities for compound potency improvements;

2. Build models for off-target proteins to enable modification of current molecules to remove unwanted activity on these off-targets and thus avoid potential side effects;

3. Build pharmacophore models (in either the presence or absence of structural information), which help define the requirements for on and off-target potency;

4. Build virtual screening model for desired target based on homology models / X-ray data / pharmacophore models (or all of the above) and carry out virtual screening of our compound libraries to identify potential high-value / high-probability molecules for prioritization in screening;

5. Build predictive models for ADME profiles within key series of interest that would be more predictive of the specific ADME profiles (e.g. solubility, clearance, protein binding, DDI potential) than available global models. Develop and deploy machine learning / artificial intelligence tools to improve speed and precision of computational predictions;

6. Incorporate publicly available information around current targets to improve mechanistic understanding of catalytic action, inhibition, and binding kinetics. Support the exploration of proposed new protein targets. Propose new protein targets by using established computational techniques as well as cutting edge techniques, including natural language processing, to explore available data and literature;

7. Design, enumerate and optimize small parallel libraries for SAR expansion based on known data and diversity of chemical space. Analyze screening libraries and carry out diversity analysis and sub-library design for optimal screening opportunities, as well as library expansion activities based on commercially available compounds;

8. Develop, deploy, and support tools for collaborative compound design and data analysis. Write computer programs to automate data analysis, provide interactive tools, and merge / enable easy transfer of disparate datasets between data-handling platforms;

9. Perform computational analysis and modeling of preclinical and clinical in vivo PK data to impact preclinical and clinical compounds on PD & efficacy;

10. Analyze protein sequence and structural information to design protein constructs to optimize potential for protein production and/or improve crystallization; and,

11. Design point mutants for cell-based target validation experiments.

     

May telecommute from any US location.

Minimum Requirements:  

A Master’s degree or foreign equivalent in Computational Chemistry, Bioinformatics, Chemistry or a closely related field plus 3 years of experience in a computational chemistry-related occupation.

Experience must include the following, which may have been gained concurrently:

1)    3 years of industry experience in computational chemistry applied to small-molecule drug discovery;

2)    3 years of experience working with multidisciplinary drug discovery teams;

3)    3 years of experience applying modelling to improve potency, selectivity, and pharmacokinetic profiles both independently, and in parallel as part of multi-parameter optimization;

4)    3 years of experience using commercial computational chemistry/cheminformatics platforms, including Cresset, CCG, OpenEye, or Schrodinger;

5)    3 years of experience applying computational chemistry tasks, including ligand docking, pharmacophore modelling, MD simulations, and QM studies;

6)    3 years of experience applying cheminformatics tasks, including building QSAR/ADMET models, statistical modeling, HTS triage, enumerating and profiling large virtual libraries, and designing small, focused libraries for synthesis;

7)    3 years of experience using Perl, C/C++, Java, or Python;

8)    3 years of experience with Linux/UNIX systems;

9)    3 years of experience utilizing cloud computing;

10) 3 years of experience using standard and advanced computational methods;

11) 3 years of experience communicating hypotheses, SAR analysis, structural designs and docking experiments using graphics tools and interactive sessions; and,

12) 3 years of experience utilizing machine learning and AI tools and techniques to improve computational chemistry and cheminformatics solutions.