Organic chemist with 30+ years in crop protection at three organizations. Background in statistical experimental design, cheminformatics and property predictions. Contributed to the development of machine-learning based models whose application to compound acquisition improved high-throughput screening hit-rates.
Organic chemist - 20+ years experience in cheminformatics.A senior-level research scientist with a broad experience in discovery research and over 20 years of experience in cheminformatics and predictive modeling. Throughout my career I have consistently applied statistical methods to projects and possess a strong skill set in data analysis and data mining. My key strengths are a background in organic chemistry, strong background in data modeling (QSAR/QSPR), experienced in predictive modeling/data mining of HTS data, a willingness to step into new roles outside my formal education with an ability to deliver results in those endeavors.
Providing consulting services in cheminformatics - similarity and diversity analysis of chemistry, clustering of chemistry. Development of structure-activity relationships to guide further activity optimization. Physical property predictions of chemistry - logP, logS, logVp to facilitate transport.
Provided computational chemistry support to project teams, developed machine-learning methodology to support compound acquisition. Resulting models provided a five-fold boost in hit-rate in the primary screens over historical hit-rates.
Initiated the application of machine-learning to compound acquisition for the crop protection group.
Lead a group of scientists in exploring various machine learning methods and various chemical descriptors to evaluate the effectiveness of enhancing high-throughput screening hit-rate in the area of crop protection.