Florentina Tofoleanu

- Comp chem lead on multiple oncology programs at all phases of drug discovery, from incipient phase to hit ID, lead opt, and drug candidate nomination - Applied structure- and ligand-based drug design principles to generate chemical matter (small molecules and peptides) with high potency and improved phys-chem/ADME properties, by using docking, free energy calculations, molecular dynamics simulations, & machine learning (ML) models. - Developed hit finding strategies in an early drug discovery project, through virtual screening, HTS, DEL screening, and hit expansions by designing bespoke compounds and libraries. - Designed the most potent chemical matter in a project that enabled functional assays and structural characterization. - Supported the company’s platform development by bridging across multidisciplinary teams in computational design, medicinal chemistry, and ML. - Interfaced with the chemistry, biophysics, and biochemistry teams, and CROs to generate chemical matter in projects, with a focus on potency and ADME properties.

- Comp chem lead for early-stage drug discovery projects in oncology, immuno-oncology, cardiovascular, ophthalmology. - Analyzed HTS data through cheminformatics tools and performed hit expansions in external vendor chemical space, through bespoke molecular design and scaffold hopping. - Structural model analysis and ML models to predict potency and selectivity - Quantum chemistry (QM) calculations to assess molecular reactivity and atropisomerism - Mentored a co-op to build QM/ML-based workflows to predict the chemical yield of Suzuki reactions. - Interfaced with medicinal and synthetic chemists, biophysicists, and biologists.

- Working in close collaboration with experimental groups within Yale University - Centrality analysis on the ligand modulation of the IGPS enzyme - Analyzed the conformational diversity of nucleotides interacting with cancer-related SAMHD1 through MD simulations and QM calculations, K-means and PCA - Grant writing. Grant on temperature-induced allostery in IGPS funded in Nov 2018. - Mentoring graduate and undergraduate students.

- Top-ranked results in SAMPL blind challenges for free energy calculations on drug-like molecules - Molecular dynamics simulations and principal component analysis of the effect of mutations upon type-2 diabetes amylin fibrils - Residence time and statistical analyses of water in MD simulations of cryo-EM and X-ray protein models - Computational and experimental determination of protein-ligand binding free energy - Invited talks at national and international conferences.

- Modeled the membrane-thinning effect of amyloid fibrils and channels using MD simulations. - Modeled the dependence of folding kinetics on the protein chain length using rate matrix analysis. - Taught graduate courses in Physics, Molecular Modeling; mentored undergraduate and graduate students.