2 research outputs found
Investigation of Take-Over Performance of Driving Tasks by the Driver due to System Failure of Semi-Automated and Assisted Driving
In the last decade a great variety of electronic systems have been developed which relieve the driver
by automating primary driving tasks, mainly in order to improve overall safety and comfort. The driving
tasks are thus split between the driver and the automation. A prerequisite for safe cooperation
between driver and the automated system is that the driver recognizes when to take over control and
begins to execute the tasks reliably which were previously performed by the automation. In this study
the driver’s take-over performance of driving tasks of semi-automated and assisted driving is
investigated on a test track in order to quantify the take-over-time depending on influencing variables
such as the degree of automation or the task that has been taken over
Combined Deep Learning and Molecular Docking Simulations Approach Identifies Potentially Effective FDA Approved Drugs for Repurposing Against SARS-CoV-2
The ongoing pandemic of Coronavirus Disease 2019
(COVID-19), the disease caused by the severe acute respiratory syndrome
coronavirus 2 (SARS-CoV-2), has posed a serious threat to global public health.
Currently no approved
drug or vaccine exists against SARS-CoV-2. Drug repurposing, represented as an effective drug discovery strategy from
existing drugs, is a time efficient approach to find effective drugs against
SARS-CoV-2 in this emergency situation. Both experimental and computational approaches are being
employed in drug repurposing with computational approaches becoming
increasingly popular and efficient. In this study, we present a robust
experimental design combining deep learning with molecular docking experiments to
identify most promising candidates from the list of FDA approved drugs that can
be repurposed to treat COVID-19. We have employed a
deep learning based Drug Target Interaction (DTI) model, called DeepDTA, with
few improvements to predict drug-protein binding affinities, represented as KIBA
scores, for 2,440 FDA approved and 8,168 investigational drugs against 24
SARS-CoV-2 viral proteins. FDA approved drugs with the highest KIBA scores were
selected for molecular docking simulations. We ran docking simulations for 168
selected drugs against 285 total predicted and/or experimentally proven active
sites of all 24 SARS-CoV-2 viral proteins. We used a recently published open source AutoDock based high
throughput screening platform virtualflow to reduce the time required to run
around 50,000 docking simulations. A list of 49 most promising FDA approved
drugs with best consensus KIBA scores and AutoDock vina binding affinity values
against selected SARS-CoV-2 viral proteins is generated. Most importantly, anidulafungin,
velpatasvir, glecaprevir, rifabutin, procaine penicillin G, tadalafil,
riboflavin 5’-monophosphate, flavin adenine dinucleotide, terlipressin,
desmopressin, elbasvir, oxatomide, enasidenib, edoxaban and selinexor
demonstrate highest predicted inhibitory potential against key SARS-CoV-2 viral
proteins.</p