Abstract
New research on Machine Learning is the subject of a report. According to news originating from Bloomington, Indiana, by NewsRx correspondents, research stated, "We present a quantum mechanical/machine learning (ML) framework based on random forest to accurately predict the ps of complex organic molecules using inexpensive density functional theory (DFT) calculations. By including physics-based features from low-level DFT calculations and structural features from our connectivity-based hierarchy (CBH) fragmentation protocol, we can correct the systematic error associated with DFT." Our news journalists obtained a quote from the research from the Department of Chemistry, "The generalizability and performance of our model are evaluated on two benchmark sets (SAMPL6 and Novartis). We believe the carefully curated input of physics-based features lessens the model's data dependence and need for complex deep learning architectures, without compromising the accuracy of the test sets."
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