HyperLoom possibilities for executing scientific workflows on the cloud

We have developed HyperLoom - a platform for defining and executing scientific workflows in large-scale HPC systems. The computational tasks in such workflows often have non-trivial dependency patterns, unknown execution time and unknown sizes of generated outputs. HyperLoom enables to efficiently execute the workflows respecting task requirements and cluster resources agnostically to the shape or size of the workflow. Although HPC infrastructures provide an unbeatable performance, they may be unavailable or too expensive especially for small to medium workloads. Moreover, for some workloads, due to HPCs not very flexible resource allocation policy, the system energy efficiency may not be optimal at some stages of the execution. In contrast, current public cloud providers such as Amazon, Google or Exoscale allow users a comfortable and elastic way of deploying, scaling and disposing a virtualized cluster of almost any size. In this paper, we describe HyperLoom virtualization and evaluate its performance in a virtualized environment using workflows of various shapes and sizes. Finally, we discuss the Hyperloom potential for its expansion to cloud environments.61140639

HyperLoom: A platform for defining and executing scientific pipelines in distributed environments

Real-world scientific applications often encompass end-to-end data processing pipelines composed of a large number of interconnected computational tasks of various granularity. We introduce HyperLoom, an open source platform for defining and executing such pipelines in distributed environments and providing a Python interface for defining tasks. HyperLoom is a self-contained system that does not use an external scheduler for the actual execution of the task. We have successfully employed HyperLoom for executing chemogenomics pipelines used in pharmaceutic industry for novel drug discovery.6

Industry-scale application and evaluation of deep learning for drug target prediction

Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. Recent works on publicly available pharmaceutical data showed that AI methods are highly promising for Drug Target prediction. However, the quality of public data might be different than that of industry data due to different labs reporting measurements, different measurement techniques, fewer samples and less diverse and specialized assays. As part of a European funded project (ExCAPE), that brought together expertise from pharmaceutical industry, machine learning, and high-performance computing, we investigated how well machine learning models obtained from public data can be transferred to internal pharmaceutical industry data. Our results show that machine learning models trained on public data can indeed maintain their predictive power to a large degree when applied to industry data. Moreover, we observed that deep learning derived machine learning models outperformed comparable models, which were trained by other machine learning algorithms, when applied to internal pharmaceutical company datasets. To our knowledge, this is the first large-scale study evaluating the potential of machine learning and especially deep learning directly at the level of industry-scale settings and moreover investigating the transferability of publicly learned target prediction models towards industrial bioactivity prediction pipelines.Web of Science121art. no. 2

2,2:6,2":6",2"-TETRAPYRIDINE PRODUCTION METHOD

FIELD: chemistry. SUBSTANCE: production method of 2 :6, 2":6", 2"-tetrapyridine, including dimerisation characterised that bipyridine N-oxide is dimerised with 0.5 eq. tert-buthyllithium and thereafter reduced with triethylphosphite. EFFECT: development of simple and economical production method of 2:6,2":6",2"-tetrapyridine. 1 cl.Предложен способ получения 2:6,2":6",2"-тетрапиридина, включающий димеризацию, отличающийся тем, что проводят димеризацию N-оксида бипиридина в присутствии 0,5 экв. трет-бутиллития с последующим восстановлением триэтилфосфитом. Технический результат: предлагаемый способ является более простым и более привлекательным с экологической точки зрения, также нет необходимости в использовании дорогих катализаторов

Derivatives of 9-phosphorylated acridine as butyrylcholinesterase inhibitors with antioxidant activity and the ability to inhibit β-amyloid self-aggregation: potential therapeutic agents for Alzheimer’s disease

We investigated the inhibitory activities of novel 9-phosphoryl-9,10-dihydroacridines and 9-phosphorylacridines against acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and carboxylesterase (CES). We also studied the abilities of the new compounds to interfere with the self-aggregation of β-amyloid (Aβ42) in the thioflavin test as well as their antioxidant activities in the ABTS and FRAP assays. We used molecular docking, molecular dynamics simulations, and quantum-chemical calculations to explain experimental results. All new compounds weakly inhibited AChE and off-target CES. Dihydroacridines with aryl substituents in the phosphoryl moiety inhibited BChE; the most active were the dibenzyloxy derivative 1d and its diphenethyl bioisostere 1e (IC50 = 2.90 ± 0.23 µM and 3.22 ± 0.25 µM, respectively). Only one acridine, 2d, an analog of dihydroacridine, 1d, was an effective BChE inhibitor (IC50 = 6.90 ± 0.55 μM), consistent with docking results. Dihydroacridines inhibited Aβ42 self-aggregation; 1d and 1e were the most active (58.9% ± 4.7% and 46.9% ± 4.2%, respectively). All dihydroacridines 1 demonstrated high ABTS•+-scavenging and iron-reducing activities comparable to Trolox, but acridines 2 were almost inactive. Observed features were well explained by quantum-chemical calculations. ADMET parameters calculated for all compounds predicted favorable intestinal absorption, good blood–brain barrier permeability, and low cardiac toxicity. Overall, the best results were obtained for two dihydroacridine derivatives 1d and 1e with dibenzyloxy and diphenethyl substituents in the phosphoryl moiety. These compounds displayed high inhibition of BChE activity and Aβ42 self-aggregation, high antioxidant activity, and favorable predicted ADMET profiles. Therefore, we consider 1d and 1e as lead compounds for further in-depth studies as potential anti-AD preparations

2-METHYLTHIO-6-NITRO-1,2,4-TRIAZOLO[5,1-C]-1,2,4-TRIAZINE-7(4H)-ONE SODIUM SALT DIHYDRATE POSSESSING ANTIVIRAL ACTIVITY

FIELD: organic chemistry, medicine, virology. SUBSTANCE: invention relates to biologically active compounds and concerns the development of a novel substance - 2-methylthio-6-nitro-1,2,4-triazolo[5,1-c]-1,2,4-triazine-7-(4H)-one sodium salt dihydrate of the formula: . This compound is designated for treatment and prophylaxis of diseases caused by viruses that are pathogenic form humans and animals. Proposed compound protects against infections caused by Rift Valley fever virus. Also, it shows activity against viruses of WEE(West Equine Encephalomyelitis), parainfluenza, respiratory-syncytium, Aujeszkys disease virus, avian infectious laryngotracheitis virus, avian influenza virus - totally against above 10 RNA- and DNA-containing viruses. The proposed compound is active in curative schedule of its using that is especially valuable. EFFECT: valuable medicinal properties of compound. 1 cl, 6 tbl, 2 dwg, 7 ex.Изобретение относится к области биологически активных соединений, касается разработки нового вещества - натриевой соли 2-метилтио-6-нитро-1,2,4-триазоло[5,1-с]-1,2,4-триазин-7-она, дигидрата и предназначено для лечения и профилактики заболеваний, вызываемых патогенными для человека и животных вирусами и имеющего формулу: Описываемое соединение защищает от инфекций, вызываемых вирусами лихорадки долины Рифт. Особенно ценно, что соединение активно при лечебной схеме применения. Оно активно в отношении вируса ЗЭЛ, парагриппа, респираторно-синцитиального вируса, вируса болезни Ауески, инфекционного ляринготрахеита птиц, вируса гриппа птиц, всего более 10 РНК и ДНК содержащих вирусов. 6 табл., 2 ил