When contemplating sustainability with regard to Chemistry, most tend to think of efforts to move toward increased use of �green� reagents, or renewable sources of energy.  Another factor that should be considered is the sustainability and growth of a field.  To promote sustainability and growth, scientific methods must be robust and reproducible.  Yet, as our scientific methods and protocols become more complex, reported methods sections (e.g., in peer-reviewed journals) are generally far too short to accurately capture the full scientific protocols employed. This threat to the scientific enterprise affects both experimental and computational fields. We have implemented the ACS Teach-Discover-Treat (TDT) initiative to address this critical issue for computational chemistry,  providing materials that will educate both novice and experienced practitioners.  As part of this effort, we have worked with experimental groups to supply relevant drug discovery datasets and sponsored a competition where groups submit tutorials and workflows that utilize publicly available software tools to analyze the datasets and propose new compounds for synthesis or screening.  The results of the computational analysis are then used to facilitate collaborations between computational and experimental research groups.  The goal of our efforts is to ensure sustainability of computational chemistry through improved reproducibility and sharing of knowledge.

ACS Teach-Discover-Treat (TDT) initiative, an effort to establish a sustainable, collaborative drug discovery community: COMP perspective

248th National Meeting (2014)

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