Aerial Talent
Associate Director / Director, Computational Chemistry (South San Francisco)
Aerial Talent, South San Francisco, California, us, 94083
Well-funded biotech in SSF seeks a Computational Chemistry Director or Associate Director with experience in CADD- and structure-based design for lead optimization of small molecules to advance the pipeline of innovative therapeutics toward the clinic.
Highlights
Onsite in South San Francisco Monday through Friday (5 days / week)
Reports to SVP, Discovery
Potential to have one or more direct reports
Two lead targets : CNS oncology + neurodegeneration
Financial runway forecasted through Spring 2027
~25 full-time employees
Responsibilities
Provide strategic leadership and hands-on execution of our CADD efforts
Collaborate closely with chemists and biologists on virtual screening, hit identification and structure-based design
Guide the optimization of drug candidates that induce protein degradation
Use machine-learning (ML) to build in silica models to predict DMPK properties
Perform and automate high throughput molecular dynamics simulations to predict protein function and protein-ligand binding
Utilize established tools and design new methods / tools
Scientific programming as needed
Required Qualifications
PhD in Computational Chemistry or related discipline
7+ years of relevant experience in drug discovery
Computer aided drug design (CADD) / structure- and ligand-based design for small molecules
Dotmatics, Schrodinger, MOE, OpenEye, or Gaussian software
QSAR, QSPR, and conformational analysis
Molecular mechanics and dynamics simulations
Preferred Skills & Knowledge
Understanding of in vitro and in vivo DMPK principles
Artificial Intelligence / Machine Learning (AI / ML) approaches to predictive modeling
Quantum mechanics methods
Statistical design of experiments (DoE) Multiparameter optimization
Experimental assay methods and technology for protein-ligand interactions
Programming with languages such as perl, python, or C++
Cheminformatics / database algorithms
Emerging drug modalities such as covalent inhibitors, bifunctionals, molecular glues and / or PROTACs
#J-18808-Ljbffr
Highlights
Onsite in South San Francisco Monday through Friday (5 days / week)
Reports to SVP, Discovery
Potential to have one or more direct reports
Two lead targets : CNS oncology + neurodegeneration
Financial runway forecasted through Spring 2027
~25 full-time employees
Responsibilities
Provide strategic leadership and hands-on execution of our CADD efforts
Collaborate closely with chemists and biologists on virtual screening, hit identification and structure-based design
Guide the optimization of drug candidates that induce protein degradation
Use machine-learning (ML) to build in silica models to predict DMPK properties
Perform and automate high throughput molecular dynamics simulations to predict protein function and protein-ligand binding
Utilize established tools and design new methods / tools
Scientific programming as needed
Required Qualifications
PhD in Computational Chemistry or related discipline
7+ years of relevant experience in drug discovery
Computer aided drug design (CADD) / structure- and ligand-based design for small molecules
Dotmatics, Schrodinger, MOE, OpenEye, or Gaussian software
QSAR, QSPR, and conformational analysis
Molecular mechanics and dynamics simulations
Preferred Skills & Knowledge
Understanding of in vitro and in vivo DMPK principles
Artificial Intelligence / Machine Learning (AI / ML) approaches to predictive modeling
Quantum mechanics methods
Statistical design of experiments (DoE) Multiparameter optimization
Experimental assay methods and technology for protein-ligand interactions
Programming with languages such as perl, python, or C++
Cheminformatics / database algorithms
Emerging drug modalities such as covalent inhibitors, bifunctionals, molecular glues and / or PROTACs
#J-18808-Ljbffr