Hokkaido University · 재료과학
Pavel Sidorov 교수의 연구실은 약물 병용 치료의 효능 예측과 항말라리아 약물 개발을 중심으로, 대량의 화합물 데이터 기반의 머신러닝 및 정량적 구조-활성 관계(QSAR/QSPR) 모델링을 수행합니다. 특히, NCI-ALMANAC과 같은 거대한 생물학적 데이터셋을 활용해 암 치료에 효과적인 약물 조합을 예측하고, 항말라리아 약물의 작용 기전과 물리화학적 특성을 정량적으로 분석하는 데 전문성을 기르고 있습니다. 연구는 화합물의 3D 구조, 반응 경로, 전자적 특성 등 다양한 화학 정보를 기반으로 한 데이터 기반 약물 설계를 목표로 합니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Drug combinations are of great interest for cancer treatment. Unfortunately, the discovery of synergistic combinations by purely experimental means is only feasible on small sets of drugs. <i>In silico</i> modeling methods can substantially widen this search by providing tools able to predict which of all possible combinations in a large compound library are synergistic. Here we investigate to which extent drug combination synergy can be predicted by exploiting the largest available dataset to d
3-Benzylmenadiones are potent antimalarial agents that are thought to act through their 3-benzoylmenadione metabolites as redox cyclers of two essential targets: the NADPH-dependent glutathione reductases (GRs) of Plasmodium-parasitized erythrocytes and methemoglobin. Their physicochemical properties were characterized in a coupled assay using both targets and modeled with QSPR predictive tools built in house. The substitution pattern of the west/east aromatic parts that controls the oxidant cha
Presently, quantum chemical calculations are widely used to generate extensive data sets for machine learning applications; however, generally, these sets only include information on equilibrium structures and some close conformers. Exploration of potential energy surfaces provides important information on ground and transition states, but analysis of such data is complicated due to the number of possible reaction pathways. Here, we present RePathDB, a database system for managing 3D structural
Abstract Background Drug combinations are of great interest for cancer treatment. Unfortunately, the discovery of synergistic combinations by purely experimental means is only feasible on small sets of drugs. In silico modeling methods can substantially widen this search by providing tools able to predict which of all possible combinations in a large compound library are synergistic. Here we investigate to which extent drug combination synergy can be predicted by exploiting the largest available
This paper presents the effort of collecting and curating a data set of 15461 molecules tested against the malaria parasite, with robust activity and mode of action annotations. The set is compiled from in-house experimental data and the public ChEMBL database subsets. We illustrate the usefulness of the dataset by building QSAR models for antimalarial activity and QSPR models for modes of actions, as well as by the analysis of the chemical space with the Generative Topographic Mapping method. T
The DOPtools (Descriptors and Optimization tools) platform is a Python library for the calculation of chemical descriptors, hyperparameter optimization, and building and validation of QSPR models.
Machine learning applications in chemistry have come a long way from the simple linear correlations of a property to a handful of descriptors, to complex models built on structural and electronic features of ensembles of molecules or reactions. Nevertheless, models built to predict the enantioselectivity of catalysts in asymmetric reactions are not yet so common. This chapter gives an overview of general approaches to treating reaction data and the recent efforts in enantioselectivity modeling u
This thesis is dedicated to the concept of the analysis of chemical space, and the application of thatconcept to antimalarial compounds. The analysis of the chemical space of antimalarial compoundshere is done with the aid of the Generative Topographic Mapping (GTM) method. A concept ofUniversal GTM maps is developed and discussed in detail in this thesis: these are maps that areable to accommodate different datasets and associated properties. Three types of maps are builtand analyzed: local, gl
The article addresses the essence, features, advantages, associated challenges, and prospects of using artificial intelligence (AI) in managing robotic systems. The relevance of this topic is substantiated by the rapid development of technologies that drive the automation of processes in industries such as manufacturing, medicine, logistics, and other key sectors. The aim of the study is to analyze existing approaches to integrating AI in robotic systems, identify positive effects, systematize t
