Practical outcomes of applying ensemble machine learning classifiers to High-Throughput Screening (HTS) data analysis and screening.

Over the years numerous papers have presented the effectiveness of various machine learning methods in analyzing drug discovery biological screening data. The predictive performance of models developed using these methods has traditionally been evaluated by assessing performance of the developed models against a portion of the data randomly selected for holdout. It has been our experience that such assessments, while widely practiced, result in an optimistic assessment. This paper describes the development of a series of ensemble-based decision tree models, shares our experience at various stages in the model development process, and presents the impact of such models when they are applied to vendor offerings and the forecasted compounds are acquired and screened in the relevant assays. We have seen that well developed models can significantly increase the hit-rates observed in HTS campaigns.

Comparative study of machine-learning and chemometric tools for analysis of in-vivo high-throughput screening data.

High-throughput screening (HTS) has become a central tool of many pharmaceutical and crop-protection discovery operations. If HTS screening is carried out at the level of the intact organism, as is commonly done in crop protection, this strategy has the potential of uncovering a completely new mechanism of actions. The challenge in running a cost-effective HTS operation is to identify ways in which to improve the overall success rate in discovering new biologically active compounds. To this end, we describe our efforts directed at making full use of the data stream arising from HTS. This paper describes a comparative study in which several machine learning and chemometric methodologies were used to develop classifiers on the same data sets derived from in vivo HTS campaigns and their predictive performances compared in terms of false negative and false positive error profiles.

InfoEvolve: moving from data to knowledge using information theory and genetic algorithms.

InfoEvolve is a unified suite of data mining and empirical modeling tools capable of discovering low-bias and low-variance solutions to complex processes. The method is based on a common set of principles involving information theory and genetic algorithms. InfoEvolve can also discover multiple strategies embedded in complex data sets for achieving a desired target or goal. This latter aspect may prove to be very useful in drug design. The paper analyzes the following: InfoEvolve from a theoretical standpoint; a conceptual overview of InfoEvolve with a short description of the modeling method; the method using the example of homogeneous identification of DNA from an analysis of its melting curve behavior; and key learnings and additional applications of the technology for both drug design and genome analysis.