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| http://dx.doi.org/10.1002/ctm2.70133 | DOI Listing |
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Deep learning-based design and experimental validation of a medicine-like human antibody library.
Brief Bioinform
November 2024
Biotherapeutics Molecule Discovery, Boehringer Ingelheim Pharmaceutical Inc., 900 Ridgebury Road, Ridgefield, CT 06877, United States.
Antibody generation requires the use of one or more time-consuming methods, namely animal immunization, and in vitro display technologies. However, the recent availability of large amounts of antibody sequence and structural data in the public domain along with the advent of generative deep learning algorithms raises the possibility of computationally generating novel antibody sequences with desirable developability attributes. Here, we describe a deep learning model for computationally generating libraries of highly human antibody variable regions whose intrinsic physicochemical properties resemble those of the variable regions of the marketed antibody-based biotherapeutics (medicine-likeness).
View Article and Find Full Text PDFMachine learning and molecular dynamics simulations predict potential TGR5 agonists for type 2 diabetes treatment.
Front Chem
January 2025
African Society for Bioinformatics and Computational Biology, Cape Town, South Africa.
Introduction: Treatment of type 2 diabetes (T2D) remains a significant challenge because of its multifactorial nature and complex metabolic pathways. There is growing interest in finding new therapeutic targets that could lead to safer and more effective treatment options. Takeda G protein-coupled receptor 5 (TGR5) is a promising antidiabetic target that plays a key role in metabolic regulation, especially in glucose homeostasis and energy expenditure.
View Article and Find Full Text PDFSynthetic data in generalizable, learning-based neuroimaging.
Imaging Neurosci (Camb)
November 2024
Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Synthetic data have emerged as an attractive option for developing machine-learning methods in human neuroimaging, particularly in magnetic resonance imaging (MRI)-a modality where image contrast depends enormously on acquisition hardware and parameters. This retrospective paper reviews a family of recently proposed methods, based on synthetic data, for generalizable machine learning in brain MRI analysis. Central to this framework is the concept of domain randomization, which involves training neural networks on a vastly diverse array of synthetically generated images with random contrast properties.
View Article and Find Full Text PDFEnhanced ensemble learning-based uncertainty and sensitivity analysis of ventilation rate in a novel radiative cooling building.
Heliyon
January 2025
Department of Energy System Engineering, Faculty of Mechanical Engineering, K.N. Toosi University of Technology, No. 15, Pardis St., Molasadra Ave., Vanak Sq., Tehran, Iran.
The rising global demand for air conditioning systems, driven by increasing temperatures and urbanization, has led to higher energy consumption and greenhouse gas emissions. HVAC systems, particularly AC, account for nearly half of building energy use, highlighting the need for efficient cooling solutions. Passive cooling, especially radiative cooling, offers potential to reduce cooling loads and improve energy efficiency.
View Article and Find Full Text PDFDeep learning enabled rapid classification of yeast species in food by imaging of yeast microcolonies.
Food Res Int
February 2025
Department of Food Science & Technology, University of California-Davis, Davis, CA 95616, USA; Department of Biological & Agricultural Engineering, University of California-Davis, Davis, CA 95616, USA. Electronic address:
Diverse species of yeasts are commonly associated with food and food production environments. The contamination of food products by spoilage yeasts poses significant challenges, leading to quality degradation and food loss. Similarly, the introduction of undesirable strains during fermentation can cause considerable challenges with the quality and progress of the fermentation process.
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