In Silico Tools for Sharing Data and Knowledge on Toxicity and Metabolism: Derek for Windows, Meteor, and Vitic

TLDR

Lhasa Limited is a not‑for‑profit organization that promotes sharing of data and knowledge in chemistry and life sciences, and accessibility is increasingly vital as in‑silico systems tackle complex phenomena with limited data. The paper provides an overview of Derek for Windows, Meteor, and Vitic, along with examples illustrating how data and knowledge sharing is integrated into their development. Derek for Windows and Meteor are knowledge‑based expert systems predicting toxicity and metabolism, Vitic is a chemically intelligent toxicity database, and the authors demonstrate sharing through data‑entry tools, nonconfidential knowledge derived from proprietary data, shared expert knowledge for prediction refinement, data‑sharing groups, and validation of predictions. Sharing chemical toxicity and metabolism data and knowledge in this manner accelerates scientific progress and reduces animal testing.

Abstract

Lhasa Limited is a not-for-profit organization that exists to promote the sharing of data and knowledge in chemistry and the life sciences. It has developed the software tools Derek for Windows, Meteor, and Vitic to facilitate such sharing. Derek for Windows and Meteor are knowledge-based expert systems that predict the toxicity and metabolism of a chemical, respectively. Vitic is a chemically intelligent toxicity database. An overview of each software system is provided along with examples of the sharing of data and knowledge in the context of their development. These examples include illustrations of (1) the use of data entry and editing tools for the sharing of data and knowledge within organizations; (2) the use of proprietary data to develop nonconfidential knowledge that can be shared between organizations; (3) the use of shared expert knowledge to refine predictions; (4) the sharing of proprietary data between organizations through the formation of data-sharing groups; and (5) the use of proprietary data to validate predictions. Sharing of chemical toxicity and metabolism data and knowledge in this way offers a number of benefits including the possibilities of faster scientific progress and reductions in the use of animals in testing. Maximizing the accessibility of data also becomes increasingly crucial as in silico systems move toward the prediction of more complex phenomena for which limited data are available.