Method overview. a) The active learning loop. We consider the entire dataset as the pool, and the oracle as the entity that holds the corresponding labels. During each iteration of the active learning loop, we select a batch of points from the pool and obtain their corresponding labels from the oracle. We then update our model using the selected batch and evaluate its performance on a holdout set. This process is repeated until the desired level of performance is achieved. b) Prediction of binding affinity is the target function for the ChEMBL and Sanofi-Aventis datasets c) Active learning batch selection. At the last layer of our model, we use either Laplace approximation or Monte Carlo dropout to compute covariances (COVLAP and COVDROP), from which an ensemble of predictions is generated. With the derived covariance matrix, we optimize batches iteratively based on their information content.

Number or required experiments for reaching model error with 10% higher than the minimum calculated RMSE for the whole training set across different methods. NC - Number of compounds in this dataset. Chron - analysis performed with batch selection using the actual order in which the data was profiled (only available for internal data). % gain estimates the improvement over the Random selection computed from (1 − NCOVDROP/NRandom) × 100. Where NCOVDROP as well as NRandom represents the average number of experiments out of 25 active learning cycles executed and a batch size of 32.

Performance of different methods for optimizing models for ADMET and affinity related datasets and batch selection strategies: a) the cell effective permeability b) lipophilicity. Panels c–f are affinity measurements to various targets proteins: c) GSK3β (ChEMBL) d) MMP3 (ChEMBL) e) Renin (Sanofi-Aventis) f) FXa (Sanofi-Aventis)