A single-stage full bridgeless boost half-bridge AC/DC converter with bidirectional switch

This paper proposes an isolated full bridgeless single stage alternating current-direct current (AC-DC) converter. The proposed converter integrates the operation of a pure bridgeless power factor correction with input boost inductor cascaded with center-tap transformer and half bridge circuit. In addition, the bidirectional switch can be driven with single control signal which further simplifies the controller circuit. It is also proved that this converter reduces the total number of components compared to some conventional circuit and semi-bridgeless circuit topologies. The circuit operation of the proposed circuit is then confirmed with the small signal model, large signal model, circuit simulation and then verified experimentally. It is designed and tested at 115 Vac, 50 Hz of input supply, and 20 Vdc output voltage with maximum output power of 100 W. In addition, the crossover distortion at the input current is minimize at high input line frequency.

MERMAID: an open source automated hit-to-lead method based on deep reinforcement learning

The hit-to-lead process makes the physicochemical properties of the hit molecules that show the desired type of activity obtained in the screening assay more drug-like. Deep learning-based molecular generative models are expected to contribute to the hit-to-lead process. The simplified molecular input line entry system (SMILES), which is a string of alphanumeric characters representing the chemical structure of a molecule, is one of the most commonly used representations of molecules, and molecular generative models based on SMILES have achieved significant success. However, in contrast to molecular graphs, during the process of generation, SMILES are not considered as valid SMILES. Further, it is quite difficult to generate molecules starting from a certain molecule, thus making it difficult to apply SMILES to the hit-to-lead process. In this study, we have developed a SMILES-based generative model that can be generated starting from a certain molecule. This method generates partial SMILES and inserts it into the original SMILES using Monte Carlo Tree Search and a Recurrent Neural Network. We validated our method using a molecule dataset obtained from the ZINC database and successfully generated molecules that were both well optimized for the objectives of the quantitative estimate of drug-likeness (QED) and penalized octanol-water partition coefficient (PLogP) optimization. The source code is available at https://github.com/sekijima-lab/mermaid.

A Hybrid Common Mode Choke Optimization Method for Input Line EMI Filter Design

In this paper, an optimization method of toroidal core-based Hybrid common-mode chokes (HCMCs) for the design of an Electromagnetic interferences (EMI) filter is proposed. A dedicated algorithm is developed using MATLAB to characterize compact HCMCs that exhibit effective CommonMode (CM) chokes with optimized leakage inductances by systematic variations in the winding patterns and geometric dimensions of their magnetic cores. It takes into consideration the physical limitations of these components as well as the constraints related to the design of EMI filters. The proposed algorithm allows through a small computational task to propose a variety of configurations for optimal HCMCs. Finite element method (FEM) simulations are conducted on the HCMCs to extract their CM and leakage inductances. The results are compared with those calculated analytically and yield a good match. The performances of optimized HCMCs are evaluated through their implementation in the designed filter. All the cases of HCMCs including the smallest one allow the EMI filter to easily qualify a power converter to an electromagnetic compatibility (EMC) standard.

Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics

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.

Enhancing the Discovery of Chemistry Theses by Registering Substances and Depositing in PubChem

Chemical substances from theses are not widely accessible as searchable machine-readable formats. In this article, we describe our workflow for extracting, registering, and sharing chemical substances from the University of Alabama theses to enhance discovery. In total, 73 theses were selected for the project, resulting in about 3,000 substances registered using the IUPAC International Chemical Identifier and deposited in PubChem as either structure-data files or Simplified Molecular-Input Line-Entry System notations. In addition to substances being deposited in PubChem, an archive copy was also deposited in the University of Alabama Institutional Repository. The PubChem records for the substance depositions include the full bibliographic reference and link to the thesis full text or thesis metadata when the full text is not yet available. Excluding mixtures, we found that 40% of the shared substances were new to PubChem at the time of deposition. We conclude this article with a detailed discussion about our experiences, challenges, and recommendations for librarians and curators engaged in sharing chemical substance data from theses and similar documents.

MERMAID: An Open Source Automated Hit-to-Lead Method Based on Deep Reinforcement Learning

<div>The hit-to-lead process makes the physicochemical properties of the hit compounds that show the desired type of activity obtained in the screening assay more drug-like. Deep learning-based molecular generative models are expected to contribute to the hit-to-lead process.</div><div>The simplified molecular input line entry system (SMILES), which is a string of alphanumeric characters representing the chemical structure of a molecule, is one of the most commonly used representations of molecules, and molecular generative models based on SMILES have achieved significant success. However, in contrast to molecular graphs, during the process of generation, SMILES are not considered as valid SMILES. Further, it is quite difficult to generate molecules starting from a certain molecule, thus making it difficult to apply SMILES to the hit-to-lead process.In this study, we have developed a SMILES-based generative model that can be generated starting from a certain compound. This method generates partial SMILES and inserts it into the original SMILES using Monte Carlo Tree Search and a Recurrent Neural Network.We validated our method using a molecule dataset obtained from the ZINC database and successfully generated molecules that were both well optimized for the objectives of the quantitative estimate of drug-likeness (QED) and penalized octanol-water partition coefficient (PLogP) optimization.</div><div>The source code is available at https: //github.com/sekijima-lab/mermaid.</div>

MERMAID: An Open Source Automated Hit-to-Lead Method Based on Deep Reinforcement Learning

<div>The hit-to-lead process makes the physicochemical properties of the hit compounds that show the desired type of activity obtained in the screening assay more drug-like. Deep learning-based molecular generative models are expected to contribute to the hit-to-lead process.</div><div>The simplified molecular input line entry system (SMILES), which is a string of alphanumeric characters representing the chemical structure of a molecule, is one of the most commonly used representations of molecules, and molecular generative models based on SMILES have achieved significant success. However, in contrast to molecular graphs, during the process of generation, SMILES are not considered as valid SMILES. Further, it is quite difficult to generate molecules starting from a certain molecule, thus making it difficult to apply SMILES to the hit-to-lead process.In this study, we have developed a SMILES-based generative model that can be generated starting from a certain compound. This method generates partial SMILES and inserts it into the original SMILES using Monte Carlo Tree Search and a Recurrent Neural Network.We validated our method using a molecule dataset obtained from the ZINC database and successfully generated molecules that were both well optimized for the objectives of the quantitative estimate of drug-likeness (QED) and penalized octanol-water partition coefficient (PLogP) optimization.</div><div>The source code is available at https: //github.com/sekijima-lab/mermaid.</div>

Human-Readable SMILES: Translating Cheminformatics to Chemistry

<div>Molecular string representations are a key asset in cheminformatics and are becoming increasingly relevant to the general chemical community, due to the steadily growing impact of Big Data and Machine Learning. Among all of the existing string representations that have been proposed, SMILES (Simplified Molecular Input Line Entry Specification) are probably the de facto standard as of today. Despite their convenience as a way to store unique molecular structures in data-bases, however, SMILES are not easy to understand for most chemists: that is, it is difficult for an untrained chemist to grasp the molecule that a SMILES is describing.</div><div><br></div><div> To mitigate this, we propose the HumanSMILES algorithm: a simple pro-cedure that can translate a SMILES string into a more interpretable name, inspired by common abbreviations and names employed in general organic chemistry. The Human-Readable SMILES can describe linear structures and general non-fused cyclic structures, with a set of naming rules that combines automated processing and chemical knowledge. The code is available open-source, as well as a web application.<br></div>

Human-Readable SMILES: Translating Cheminformatics to Chemistry

<div>Molecular string representations are a key asset in cheminformatics and are becoming increasingly relevant to the general chemical community, due to the steadily growing impact of Big Data and Machine Learning. Among all of the existing string representations that have been proposed, SMILES (Simplified Molecular Input Line Entry Specification) are probably the de facto standard as of today. Despite their convenience as a way to store unique molecular structures in data-bases, however, SMILES are not easy to understand for most chemists: that is, it is difficult for an untrained chemist to grasp the molecule that a SMILES is describing.</div><div><br></div><div> To mitigate this, we propose the HumanSMILES algorithm: a simple pro-cedure that can translate a SMILES string into a more interpretable name, inspired by common abbreviations and names employed in general organic chemistry. The Human-Readable SMILES can describe linear structures and general non-fused cyclic structures, with a set of naming rules that combines automated processing and chemical knowledge. The code is available open-source, as well as a web application.<br></div>