Machine Learning Models for Predicting Monoclonal Antibody Biophysical Properties from Molecular Dynamics Simulations and Deep Learning-Based Surface Descriptors

Monoclonal antibodies (mAbs) have found extensive applications
and development in treating various diseases. From the pharmaceutical
industry’s perspective, the journey from the design and development
of mAbs to clinical testing and large-scale production is a highly
time-consuming and resource-intensive process. During the research
and development phase, assessing and optimizing the developability
of mAbs is of paramount importance to ensure their success as candidates
for therapeutic drugs. The critical factors influencing mAb development
are their biophysical properties, such as aggregation propensity,
solubility, and viscosity. This study utilized a data set comprising
12 biophysical properties of 137 antibodies from a previous study
(Proc Natl Acad Sci USA. 114(5):944–949, 2017). We employed
full-length antibody molecular dynamics simulations and machine learning
techniques to predict experimental data for these 12 biophysical properties.
Additionally, we utilized a newly developed deep learning model called
DeepSP, which directly predicts the dynamical and structural properties
of spatial aggregation propensity and spatial charge map in different
antibody regions from sequences. Our research findings indicate that
the machine learning models we developed outperform previous methods
in predicting most biophysical properties. Furthermore, the DeepSP
model yields similar predictive results compared to molecular dynamic
simulations while significantly reducing computational time. The code
and parameters are freely available at https://github.com/Lailabcode/AbDev. Also, the webapp, AbDev, for 12 biophysical properties prediction
has been developed and provided at https://devpred.onrender.com/AbDev.