IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads

The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and $2-3 billion to deliver one new drug. This is both too expensive and too slow, especially in emergencies like the COVID-19 pandemic. In silico methodologies need to be improved both to select better lead compounds, so as to improve the efficiency of later stages in the drug discovery protocol, and to identify those lead compounds more quickly. No known methodological approach can deliver this combination of higher quality and speed. Here, we describe an Integrated Modeling PipEline for COVID Cure by Assessing Better LEads (IMPECCABLE) that employs multiple methodological innovations to overcome this fundamental limitation. We also describe the computational framework that we have developed to support these innovations at scale, and characterize the performance of this framework in terms of throughput, peak performance, and scientific results. We show that individual workflow components deliver 100 × to 1000 × improvement over traditional methods, and that the integration of methods, supported by scalable infrastructure, speeds up drug discovery by orders of magnitudes. IMPECCABLE has screened ∼1011 ligands and has been used to discover a promising drug candidate. These capabilities have been used by the US DOE National Virtual Biotechnology Laboratory and the EU Centre of Excellence in Computational Biomedicine. © 2021 ACM.

Evaluation of Proprietary Social VR Platforms for Use in Distance Learning

Distance learning in form of video chats or the streaming of recorded lectures are common ways for schools and universities to maintain teaching under the restrictions of the global SARS-CoV-2 pandemic. One possibility to improve distance learning can be the use of virtual reality (VR). To this day there are no common guidelines for educators for using VR, and therefore no recommendations to decide which platform to use and what specific requirements they entail. This paper presents an evaluation of currently available proprietary social VR platforms for the use in distance learning. Based on results of expert interviews (n= 4 ) with educators and an online survey (n= 92 ) of students, we identified ten relevant criteria for teaching in VR. We compiled a list of 155 currently available proprietary social VR platforms, which we filtered and evaluated. The results indicate that current social VR platforms can provide an easy and affordable way for educators to access the usage of VR in education, although we could not determine a single best social VR platform. Our recommendations rather provide different most suitable platforms for particular learning applications depending to respective educational requirements. Our research provides a guideline for interested educators in order to find an affordable entry into the usage of VR in education. Building on this, future research can shed light on questions of didactic effects, user-friendliness, or data protection. © 2021, Springer Nature Switzerland AG.