To select the most promising screening hits from antibody and VHH display campaigns for subsequent in-depth profiling and optimization, it is highly desirable to assess and select sequences on properties beyond only their binding signals from the sorting process. In addition, developability risk criteria, sequence diversity and the anticipated complexity for sequence optimization are relevant attributes for hit selection and optimization. Here, we describe an approach for the in silico developability assessment of antibody and VHH sequences. This method not only allows for ranking and filtering multiple sequences with regard to their predicted developability properties and diversity, but also visualizes relevant sequence and structural features of potentially problematic regions and thereby provides rationales and starting points for multi-parameter sequence optimization.
Explainable and accurate predictions of bioactivity values are indispensable to accelerate drug design and reduce attrition during drug development. To enhance the accuracy of predictions, a new algorithmic approach was designed, which unites the advantages of matched molecular series and supervised machine learning (ML) techniques. This approach named Network Balance Scaling (NBS) employs convex optimization on matched molecular series networks enriched with ML data. By applying NBS, the performance of supervised ML methods can be improved, and predictions can be rationalized following the network of similar compounds.
The approach was validated on the freely available MoleculeNet benchmark with respective ML models as well as on real-world targets and corresponding ML models provided by Merck KGaA. In all cases, when combining NBS with supervised ML models, we observe a substantial improvement in the different activity and physicochemical data sets and bioactivity-related application scenarios. The open-source code of NBS can be found freely available at https://github.com/rareylab/NetworkBalanceScaling.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.