  The accurate screening of candidate drug ligands against target proteins
through computational approaches is of prime interest to drug development
efforts. Such virtual screening depends in part on methods to predict the
binding affinity between ligands and proteins. Many computational models for
binding affinity prediction have been developed, but with varying results
across targets. Given that ensembling or meta-modeling methods have shown great
promise in reducing model-specific biases, we develop a framework to integrate
published force-field-based empirical docking and sequence-based deep learning
models. In building this framework, we evaluate many combinations of individual
base models, training databases, and several meta-modeling approaches. We show
that many of our meta-models significantly improve affinity predictions over
base models. Our best meta-models achieve comparable performance to
state-of-the-art deep learning tools exclusively based on structures, while
allowing for improved database scalability and flexibility through the explicit
inclusion of features such as physicochemical properties or molecular
descriptors. Overall, we demonstrate that diverse modeling approaches can be
ensembled together to gain improvement in binding affinity prediction.
