- PhD in bioinformatics, biostatistics, computational biology or similar, with a strong publication record. Alternately, a PhD in molecular biology combined with a very strong record of high-throughput data analysis, supported by publication in this area.
- A solid understanding of the relevant concepts and cancer biology and genetics.
- Broad experience with data generated by one or more high-throughput molecular assays: microarrays, next-generation sequencing, multiplexed qPCR, mass spectrometry proteomics, etc.
- An understanding of the statistical principles behind current best practices in the field. Experience with biomarker discovery and evaluation is a plus.
- Expertise in the use of a high-level programming language such as R, MATLAB, Python or Perl for complex data analysis.
- Exceptionally strong communication, data presentation and visualization skills.
- Ability to work both independently and collaboratively, and to handle several concurrent, fast-paced projects.
Knowledge of one or many of the following products:
Conformation Generation and Clustering
• MacroModel – gas phase and solution
• ConfGen – bioactive conformation generation
• XCluster – conformation clustering
Property Generation and Filtering
• QikProp – descriptor generation and ADME/Tox prediction
• Ligand Properties & Filtering
1D/2D to 3D Structure Generation
• LigPrep
• Epik – fast pKa and tautomer prediction
Molecular Mechanics
• MM2, MM3, AMBER, AMBER94, MMFF, MMFFs, OPLS,
and OPLS-AA Force Fields
• GB/SA Solvation Model
• MacroModel
• Impact
• MINTA – conformational free energies
• Force Field Viewer
Molecular Dynamics
• Desmond – explicit solvent MD
• Impact – implicit and explicit solvent MD
Quantum Mechanics
• Jaguar
• Jaguar pKa
• Hydrogen Bond Calculator
Structural Biology – Crystallography
• PrimeX – protein crystal structure refinement
• Protein Structure Analysis
Protein Modeling and Bioinformatics
• Prime Homology Modeling
• Prime Loop and Side-Chain Prediction
• Prime Sequence Alignment
• Prime Fold Recognition
Molecular Mechanics
• MM2, MM3, AMBER, AMBER94, MMFF, MMFFs, OPLS,
and OPLS-AA Force Fields
• GB/SA Solvation Model
• Prime – protein structure prediction
• MacroModel
• Large-Scale Low-Mode (LLMOD) conformational sampling
• Impact
Molecular Dynamics • Force Field Viewer
• Desmond – explicit solvent MD
• Impact – implicit and explicit solvent MD
Monte Carlo Simulations
• MCPRO+
QM/MM
• QSite
Ligand-Based Discovery
• Phase – pharmacophore modeling
• Phase Shape
• Phase Multiple Binding Mode Predictor
• Phase commercially available compound database
• Flexible Ligand Superposition
Fragment-Based Discovery
• Glide – docking and scoring
• CombiGlid e – combinatorial technology
• Phase – pharmacophore modeling
• Rule-based Molecule Fragmenting
• Fragment Joining/Linking
• Ligand Efficiency (LE) Metrics
• BREED – ligand hybridization
Structure-Based Discovery
• Glide – docking and scoring
• Virtual Screening Workflow (VSW)
• SiteMap – protein binding site identification and analysis
Covalent Docking
• Protein Preparation Wizard
Protein Structure Alignment
• GPCR Modeling
• Core Hopping – receptor-based
• Core Hopping – attachment- and shape-based
Cheminformatics
• Canvas
Cheminformatics
• Canvas
2D/3D QSAR
• Phase – pharmacophore modeling
• Strike – statistical modeling
• QikProp – property generation
Combinatorial Chemistry
• CombiGlide R-group Evaluator
Fragment-Based Design
• Glide – docking and scoring
• Rule-based Molecule Fragmenting
• Fragment Joining/Linking
• Ligand Efficiency (LE) Metrics
• BREED – ligand hybridization
Ligand-Based Design
• Phase – pharmacophore modeling
Structure-Based Design
• Glide – docking and scoring
• Induced Fit Docking
• Prime MM/GBSA
• Core Hopping
• CombiGlide Screening
• HERG Modeling with Induced Fit Docking
• SiteMap – protein binding site identification and analysis
• Embrace – post-docking refinement
• Hydrogen-Bond Calculator
• Liaison – linear interaction approximation
• MCPRO+
• QM-Polarized Ligand Docking
• SIFt – Structural Interaction Fingerprints
• QSite – QM/MM
• Ligand Designer
Molecular Visualization
• Maestro – molecular modeling graphical interface
• Glide XP Visualizer
• 2D Viewer
Application Deployment Interfaces
• Maestro – molecular modeling interface
• Canvas – cheminformatics
• KNIME Extensions
Scripting, Methods Development, and Deployment
• Python API
• Maestro Command Language
Workflow/Pipelining
• KNIME Extensions
• Python Pipelining Infrastructure
Medicinal Chemistry Applications
• 2D Viewer
• 3D Builder
• Maestro Elements – computational task interface designed
• Ligand Designer
- Analyze large-scale genomic data, such as microarray or next-generation sequencing (Exome, Whole genome and RNA-seq) data from patient samples to support patient stratification and biomarker selection for multiple development programs.
- Perform phylogenetic analysis of viral sequences from patient samples to understand drug resistance mechanisms.
- Develop bioinformatic software, analysis pipelines and applications to make pharmacogenomics results and findings available to research and development groups.
- A PhD in bioinformatics, computational biology, genomics, computer science, statistics or a related field with strong peer-reviewed publication record. Or a MS degree with 3+ years of work experience.
- Proficient in at least one of following programing languages: C, C++, Java, Perl, Python and Shell in a Linux environment.
- Hands-on experience in analyzing next-generation sequencing data is required.
- Proficient in statistical data analysis of genomic data using R/boiconductor.
- Outstanding team player with strong interpersonal and communication skills.
- Knowledge in virology, immunology, oncology or experience in web / database development is a plus.
Send in your resume: [email protected]
