Black-box optimization refers to the optimization problem whose objective function and/or constraint sets are either unknown, inaccessible, or non-existent. In many applications, especially with the involvement of humans, the only way to access the optimization problem is through performing physical experiments with the available outcomes being the preference of one candidate with respect to one or many others. Accordingly, the algorithm so-called Active Preference Learning has been developed to exploit this specific information in constructing a surrogate function based on standard radial basis functions, and then forming an easy-to-solve acquisition function which repetitively suggests new decision vectors to search for the optimal solution. Based on this idea, our approach aims to extend the algorithm in such a way that can exploit further information effectively, which can be obtained in reality such as: 5-point Likert type scale for the outcomes of the preference query (i.e., the preference can be described in not only "this is better than that" but also "this is much better than that" level), or multiple outcomes for a single preference query with possible additive information on how certain the outcomes are. The validation of the proposed algorithm is done through some standard benchmark functions, showing a promising improvement with respect to the state-of-the-art algorithm.

One of the key requirements for incorporating machine learning into the drug discovery process is complete reproducibility and traceability of the model building and evaluation process. With this in mind, we have developed an end-to-end modular and extensible software pipeline for building and sharing machine learning models that predict key pharma-relevant parameters. The ATOM Modeling PipeLine, or AMPL, extends the functionality of the open source library DeepChem and supports an array of machine learning and molecular featurization tools. We have benchmarked AMPL on a large collection of pharmaceutical datasets covering a wide range of parameters. Our key findings include: Physicochemical descriptors and deep learning-based graph representations are significantly better than traditional fingerprints to characterize molecular features Dataset size is directly correlated to performance of prediction: single-task deep learning models only outperform shallow learners if there is enough data.

One of the key requirements for incorporating machine learning into the drug discovery process is complete reproducibility and traceability of the model building and evaluation process. With this in mind, we have developed an end-to-end modular and extensible software pipeline for building and sharing machine learning models that predict key pharma-relevant parameters. The ATOM Modeling PipeLine, or AMPL, extends the functionality of the open source library DeepChem and supports an array of machine learning and molecular featurization tools. We have benchmarked AMPL on a large collection of pharmaceutical datasets covering a wide range of parameters. As a result of these comprehensive experiments, we have found that physicochemical descriptors and deep learning-based graph representations significantly outperform traditional fingerprints in the characterization of molecular features. We have also found that dataset size is directly correlated to prediction performance, and that single-task deep learning models only outperform shallow learners if there is sufficient data. Likewise, dataset size has a direct impact on model predictivity, independent of comprehensive hyperparameter model tuning. Our findings point to the need for public dataset integration or multi-task/transfer learning approaches. Lastly, we found that uncertainty quantification (UQ) analysis may help identify model error; however, efficacy of UQ to filter predictions varies considerably between datasets and featurization/model types. AMPL is open source and available for download at http://github.com/ATOMconsortium/AMPL.