Online user education

Purpose: This study aims to describe online user education activities during the COVID-19 pandemic at the UIN Sunan Kalijaga Yogyakarta Library. Design/Methodology/Approach: The approach taken in this paper is an empirical approach related to activities at the UIN Sunan Kalijaga Library. Findings: This paper provides an overview of the impact of the pandemic on services at the UIN Sunan Kalijaga Library. It also highlights the implementation of online user education during the COVID-19 pandemic at the UIN Sunan Kalijaga Library which was previously carried out physically. It further observes user behavior after online user education is implemented. Originality/Values: The library's effort to organize online user education is one of the innovations made by the library of UIN Sunan Kalijaga in the midst of the COVID-19 pandemic. Online user education must continue to be carried out to help users get to know the library orientation at the UIN Sunan Kalijaga Library. The implementation of online user education at the UIN Sunan Kalijaga Library can be imitated and can be used by other libraries as a model for implementing user education during the pandemic.

Machine learning-based library design improves packaging and diversity of adeno-associated virus (AAV) libraries

AAVs hold tremendous promise as delivery vectors for clinical gene therapy. Yet the ability to design libraries comprising novel and diverse AAV capsids, while retaining the ability of the library to package DNA payloads, has remained challenging. Deep sequencing technologies allow millions of sequences to be assayed in parallel, enabling large-scale probing of fitness landscapes. Such data can be used to train supervised machine learning (ML) models that predict viral properties from sequence, without mechanistic knowledge. Herein, we leverage such models to rationally trade-off library diversity with packaging capability. In particular, we show a proof-of-principle application of a general approach for ML-guided library design that allows the experimenter to rationally navigate the trade-off between sequence diversity and fitness of the library. Consequently, this approach, instantiated with an AAV capsid library designed for packaging, enables the selection of starting libraries that are more likely to yield success in downstream selections for therapeutics and beyond. We demonstrated this increased success by showing that the designed libraries are able to more easily infect primary human brain tissue. We expect that such ML-guided design of AAV libraries will have broad utility for the development of novel variants for therapeutic applications in the near future.

Analysis of the uncharted, druglike property space by self-organizing maps

Physicochemical properties are fundamental to predict the pharmacokinetic and pharmacodynamic behavior of drug candidates. Easily calculated descriptors such as molecular weight and logP have been found to correlate with the success rate of clinical trials. These properties have been previously shown to highlight a sweet-spot in the chemical space associated with favorable pharmacokinetics, which is superior against other regions during hit identification and optimization. In this study, we applied self-organizing maps (SOMs) trained on sixteen calculated properties of a subset of known drugs for the analysis of commercially available compound databases, as well as public biological and chemical databases frequently used for drug discovery. Interestingly, several regions of the property space have been identified that are highly overrepresented by commercially available chemical libraries, while we found almost completely unoccupied regions of the maps (commercially neglected chemical space resembling the properties of known drugs). Moreover, these underrepresented portions of the chemical space are compatible with most rigorous property filters applied by the pharma industry in medicinal chemistry optimization programs. Our results suggest that SOMs may be directly utilized in the strategy of library design for drug discovery to sample previously unexplored parts of the chemical space to aim at yet-undruggable targets. Graphic abstract