Project Summary The objective of ‘MegaPredict’ is to enable scientists to generate predictions for a natural product (or any molecule) and identify targets for efficacy assessment as well as identify any potential liabilities. We are building on our previous work which has compiled a comprehensive collection of datasets for structure-activity data for a broad variety of disease targets and other properties, in a form ready for model building. All of these models utilize the many sources of curated open data, including ChEMBL, ToxCast etc. We have developed a prototype of MegaPredict that utilizes Bayesian algorithm and ECFP6 fingerprints to output a list of prioritized ‘targets’. We realize that neither the algorithm or the descriptors may be optimal therefore we propose to address this as we validate MegaPredict and develop a product over this proposal. Our team is suitably qualified to develop the software needed and we will leverage our large collaborator network to assist us in validating the activity of compounds. We will initially create a script to take a natural product and score it against many thousands of machine learning models then rank the outputs to propose efficacy targets. We will use over 12,000 ChEMBL derived target-assay / bioactivity groups extracted from the ChEMBL v24 database, as well as EPA Tox21 measurements and other public datasets, using methodology that we have already partially developed. We can repeat this process for over 200 published compounds and access the outputs versus what is known. We intend to compare how the approach performs with synthetic drugs or drug-like compounds as well as natural products. We will assess whether other machine learning algorithms and molecular descriptors can improve predictions. As we generate machine learning models such as Linear Logistic Regression, AdaBoost Decision Tree, Random Forest, Support Vector Machine and deep neural networks (DNN) of varying depth we will assess the predictions for natural products and compare with the Bayesian approach. We will compare ECFP6 with other 2D, 3D descriptors and physicochemical properties in order to identify the optimal combination for generating predictions for natural products and compare how this differs for synthetic compounds. We will validate our predictions for natural product efficacy assessment. We will work closely with multiple academic groups to generate predictions for at least 20 natural products of interest against over 20 different targets or diseases. Our goal will be to identify potential targets that were previously unknown and then generate in vitro data inhouse or with academic collaborators. Develop a prototype user interface for input of a structure, processing an input molecule and output of prioritized targets and liabilities. We have developed multiple software prototypes (e. Assay Central, MegaTox, etc.) previously and will ensure a user-friendly interface and develop new visualization methods and algorithms ...