InChI Full List of Publications & Presentation
Pietro Cozzini; Francesca Cavaliere; Giulia Spaggiari; Gianluca Morelli; Marco Riani
Chemosphere 292 (2022) 133422.
According to Eurostat, the EU production of chemicals hazardous to health reached 211 million tonnes in 2019. Thus, the possibility that some of these chemical compounds interact negatively with the human endocrine system has received, especially in the last decade, considerable attention from the scientific community. It is obvious that given the large number of chemical compounds it is impossible to use in vitro/in vivo tests for identifying all the possible toxic interactions of these chemicals and their metabolites. In addition, the poor availability of highly curated databases from which to retrieve and download the chemical, structure, and regulative information about all food contact chemicals has delayed the application of in silico methods. To overcome these problems, in this study we use robust computational approaches, based on a combination of highly curated databases and molecular docking, in order to screen all food contact chemicals against the nuclear receptor family in a cost and time-effective manner.
Minh Pham; Hao Li; Yongke Yuan; Chengcheng Mou; Kandethody Ramachandran; Zichen Xu; Yicheng Tu
Massively parallel systems, such as Graphics Pro- cessing Units (GPUs), are becoming increasingly important in today’s data-intensive computing environments. Due to the high level of parallelism, there are unique challenges in developing system software on massively parallel hardware to efficiently support a large number of parallel threads. One such challenge is designing a dynamic memory allocator whose task is to allocate memory chunks to requesting threads at runtime. A traditional design of a memory allocator involves maintaining a global data structure, such as a list of free pages. However, the centralized data structure can easily become a bottleneck in a massively parallel system. The bottleneck still exists when multiple queues are maintained, as done in state-of-the-art GPU memory allocation solutions. In this paper, we present a novel approach for designing dynamic memory allocation without a centralized data structure. At runtime, the threads follow a random search procedure to locate free pages. We develop mathematical models to demonstrate that our methods achieve asymptotically lower latency than the traditional queue- based design. Extensive experiments show consistency to our mathematical models and demonstrate that our solutions can achieve up to two orders of magnitude improvement in latency over the best-known existing solutions.
Tagir Akhmetshin; Arkadii Lin; Daniyar Mazitov; Evgenii Ziaikin; Timur Madzhidov; Alexandre Varnek
Graph-based architectures are becoming increasingly popular as a tool for structure generation. Here, we introduce a novel open-source architecture HyFactor which is inspired by previously reported DEFactor architecture and based on the hydrogen labeled graphs. Since the original DEFactor code was not available, its new implementation (ReFactor) was prepared in this work for the benchmarking purpose. HyFactor demonstrates its high performance on the ZINC 250K MOSES and ChEMBL data set and in molecular generation tasks, it is considerably more effective than ReFactor. The code of HyFactor and all models obtained in this study are publicly available from our GitHub repository: https://github.com/Laboratoire-de- Chemoinformatique/hyfactor
Rengul Cetin-Atalay; Deniz Cansen Kahraman; Esra Nalbat; Ahmet Sureyya Rifaioglu; Ahmet Atakan; Ataberk Donmez; Heval Atas; M. Volkan Atalay; Aybar C. Acar; Tunca Doğan
J Gastrointestinal Cancer (2021)
Computational approaches have been used at different stages of drug development with the purpose of decreasing the time and cost of conventional experimental procedures. Lately, techniques mainly developed and applied in the field of artificial intelligence (AI), have been transferred to different application domains such as biomedicine. In this study, we conducted an investigative analysis via data-driven evaluation of potential hepatocellular carcinoma (HCC) therapeutics in the context of AI-assisted drug discovery/repurposing. First, we discussed basic concepts, computational approaches, databases, modeling approaches, and featurization techniques in drug discovery/repurposing. In the analysis part, we automatically integrated HCC-related biological entities such as genes/proteins, pathways, phenotypes, drugs/compounds, and other diseases with similar implications, and represented these heterogeneous relationships via a knowledge graph using the CROssBAR system.
Type: Drug Design
Donald R. Burgess Jr., Valeri I. Babushok, Jeffrey A. Manion
Int. J. Chem. Kinet. 61 (2021)
In this work, we compiled and evaluated rate expressions for reactions relevant to the decomposition and combustion of CH2F2 (difluoromethane, refrigerant R-32) in CH2F2/O2/N2 flames. The recommended values have been used in premixed flame calculations, reported elsewhere, to model experimentally derived burning velocities determined using a constant volume spherical flame method. In this work, we also provide a detailed description of the reaction pathways for decomposition and combustion of CH2F2. This work is part of a larger effort at NIST to characterize and predict the flammability of new refrigerant working fluids and their blends for consideration as replacements of current refrigerants with high global warming potentials.
Vaz, Joel Markus; Balaji, S.
Mol Divers 25 1569-1584 (2021).
Convolutional neural networks (CNNs) have been used to extract information from various datasets of different dimensions. This approach has led to accurate interpretations in several subfields of biological research, like pharmacogenomics, addressing issues previously faced by other computational methods. With the rising attention for personalized and precision medicine, scientists and clinicians have now turned to artificial intelligence systems to provide them with solutions for therapeutics development. CNNs have already provided valuable insights into biological data transformation. Due to the rise of interest in precision and personalized medicine, in this review, we have provided a brief overview of the possibilities of implementing CNNs as an effective tool for analyzing one-dimensional biological data, such as nucleotide and protein sequences, as well as small molecular data, e.g., simplified molecular-input line-entry specification, InChI, binary fingerprints, etc., to categorize the models based on their objective and also highlight various challenges. The review is organized into specific research domains that participate in pharmacogenomics for a more comprehensive understanding. Furthermore, the future intentions of deep learning are outlined.
Handsel, J., Matthews, B., Knight, N.J., Coles, S. J.
J Cheminform 13, 79 (2021).
Expert Opinion on Drug Disc (2021).
The popularity and success of advanced AI methods like deep neural networks has led to novel ways for exploring chemical space. Their opaque nature poses challenges for model evaluation regarding novelty, uniqueness, and distribution of the chemical space covered. However, these methods also promise to be able to explore uncharted chemical space in novel ways that do not rely directly on structural similarity.
Type: Drug Design
Rui P.S. Patrício, Paula A. Videirab, Florbela Pereira
Bioorganic & Medicinal Chemistry 53, 116530 (2022).
Colorectal cancer (CRC) is the third most detected cancer and the second foremost cause of cancer deaths in the world. Intervention targeting p53 provides potential therapeutic strategies, but thus far no p53-based therapy has been successfully translated into clinical cancer treatment. Here we developed a Quantitative Structure–Activity Relationships (QSAR) classification models using empirical molecular descriptors and fingerprints to predict the activity against the p53 protein, using the potency value with the active or inactive label, were developed. These models were built using in total 10,505 molecules that were extracted from the ChEMBL, ZINC and Reaxys® databases, and recent literature. Three machine learning (ML) techniques e.g., Random Forest, Support Vector Machine, Convolutional Neural Network were explored to build models for p53 inhibitor prediction. The performances of the models were successfully evaluated by internal and external validation. Moreover, based on the best in silico p53 model, a virtual screening campaign was carried out using 1443 FDA-approved drugs that were extracted from the ZINC database. A list of virtual screening hits was assented on base of some limits established in this approach, such as: (1) probability of being active against p53; (2) applicability domain; (3) prediction of the affinity between the p53, and ligands, through molecular docking. The most promising according to the limits established above was dihydroergocristine. This compound revealed cytotoxic activity against a p53-expressing CRC cell line with an IC50 of 56.8 µM. This study demonstrated that the computer-aided drug design approach can be used to identify previously unknown molecules for targeting p53 protein with anti-cancer activity and thus pave the way for the study of a therapeutic solution for CRC.
Type: Drug Design