With the advent of more powerful and accurate modeling techniques in the field of small molecule drug discovery, it has become increasingly important to visualize modeling results side by side with informatics and experimental data during the iterative processes of drug design. LiveDesign enables chemists to do just this, delivering modelling and informatics results for new ideas or existing compounds in a highly collaborative real-time fashion.<br/><br/>Recently emerging at the forefront of computational method, machine learning has proven to be an effective tool for enhancing any data driven project, regardless of industry. The drug discovery world has only just begun to tap into the potential of AI for the drug development process, and it shows promising signs for allowing the pharmaceutical world to iterate faster and move quicker by predicting key values that would normally take substantially more time and resources to obtain. LiveDesign is uniquely positioned to deliver state of the art modern machine learning algorithms and seamlessly incorporate them into existing workflows. Situated at the intersection of modeling and experimental data, LiveDesign can utilize this robust data set in a project access controlled way to train these advanced algorithms to make effective predictions and put a drug project at the forefront of the computational drug discovery world.<br/><br/>Herein we present LiveDesign’s usage as a platform for delivering machine learning techniques.

LiveDesign as a machine-learning platform for collaborative drug discovery

255th National Meeting (2018)

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.