A5034752225- Computational Drug Discovery Methods
- vaccines and immunoinformatics approaches
- Machine Learning in Materials Science
- Genetics, Bioinformatics, and Biomedical Research
- Machine Learning in Bioinformatics
- Antimicrobial Peptides and Activities
- Microbial Natural Products and Biosynthesis
- SARS-CoV-2 and COVID-19 Research
- Immunotherapy and Immune Responses
- Malaria Research and Control
- Synthesis and Catalytic Reactions
- Biochemical and Structural Characterization
- Histone Deacetylase Inhibitors Research
- Peptidase Inhibition and Analysis
- Monoclonal and Polyclonal Antibodies Research
- Crystallization and Solubility Studies
- Synthesis and Biological Evaluation
- Molecular Biology Techniques and Applications
- Chemical synthesis and alkaloids
- X-ray Diffraction in Crystallography
- Chemical Synthesis and Analysis
- Click Chemistry and Applications
University of California, San Francisco
2024
UCSF Helen Diller Family Comprehensive Cancer Center
2024
University of Central Florida
2020-2022
Florida College
2021
Pasteur Institute of Iran
2017
Antimalarial drugs are becoming less effective due to the emergence of drug resistance. Resistance has been reported for all available malaria drugs, including artemisinin, thus creating a perpetual need alternative candidates. The traditional discovery approach high throughput screening (HTS) large compound libraries identification new leads is time-consuming and resource intensive. While virtual in silico solution this problem, however, generalization models not ideal. Artificial...
Deep learning's automatic feature extraction has proven to give superior performance in many sequence classification tasks. However, deep learning models generally require a massive amount of data train, which the case Hemolytic Activity Prediction Antimicrobial Peptides creates challenge due small available data.Three different datasets for hemolysis activity prediction therapeutic and antimicrobial peptides are gathered AMPDeep pipeline is implemented each. The result demonstrate that...
Abstract Deep learning’s automatic feature extraction has been a revolutionary addition to computational drug discovery, infusing both the capabilities of learning abstract features and discovering complex molecular patterns via from data. Since biological chemical knowledge are necessary for overcoming challenges data curation, balancing, training, evaluation, it is important databases contain information regarding exact target disease each bioassay. The existing depositories such as...
MicroRNAs are recognized as key drivers in many cancers but targeting them with small molecules remains a challenge. We present RiboStrike, deep-learning framework that identifies against specific microRNAs. To demonstrate its capabilities, we applied it to microRNA-21 (miR-21), known driver of breast cancer. ensure selectivity toward miR-21, performed counter-screens miR-122 and DICER. Auxiliary models were used evaluate toxicity rank the candidates. Learning from various datasets, screened...
There is an urgent need to develop new efficacious antimalarials address the emerging drug-resistant clinical cases. Our previous phenotypic screening identified styrylquinoline UCF501 as a promising antimalarial compound. To optimize UCF501, we herein report detailed structure–activity relationship study of 2-arylvinylquinolines, leading discovery potent, low nanomolar antiplasmodial compounds against Plasmodium falciparum CQ-resistant Dd2 strain, with excellent selectivity profiles...
Cyclic tetrapeptide histone deacetylase inhibitors represent a promising class of antiplasmodial agents that epigenetically disrupt wide range cellular processes in Plasmodium falciparum. Unfortunately, certain limitations, including reversible killing effects and host cell toxicity, prevented these from further development clinical use as antimalarials. In this study, we present series cyclic analogues derived primarily the fungus Wardomyces dimerus inhibit P. falciparum with low nanomolar...
Identification of autoimmune processes and introduction new autoantigens involved in the pathogenesis multiple sclerosis (MS) can be helpful design drugs to prevent unresponsiveness side effects patients. To find significant changes, we evaluated autoantibody repertoires newly diagnosed relapsing-remitting MS patients (NDP) those receiving disease-modifying therapy (RP). Through a random peptide phage library, panel NDP- RP-specific peptides was identified, producing two protein data sets...
Deep learning’s automatic feature extraction has proven its superior performance over traditional fingerprint-based features in the implementation of virtual screening models. However, these models face multiple challenges field early drug discovery, such as over-training and generalization to unseen data, due inherently unbalanced small datasets. In this work, TranScreen pipeline is proposed, which utilizes transfer learning a collection weight initializations overcome challenges. An amount...
MicroRNAs are recognized as key drivers in many cancers, but targeting them with small molecules remains a challenge. We present RiboStrike, deep learning framework that identifies against specific microRNAs. To demonstrate its capabilities, we applied it to microRNA-21 (miR-21), known driver of breast cancer. ensure the selected only targeted miR-21 and not other microRNAs, also performed counter-screen DICER, an enzyme involved microRNA biogenesis. Additionally, used auxiliary models...
Abstract Background: Deep learning’s automatic feature extraction has proven to give superior performance in many sequence classification tasks. However, deep learning models generally require a massive amount of data train, which the case Hemolytic Activity Prediction Antimicrobial Peptides creates challenge due small available data. Results: Three different benchmarks for hemolysis activity prediction therapeutic and antimicrobial peptides are gathered AMPDeep pipeline is implemented each....
Abstract Deep learning’s automatic feature extraction has been a revolutionary addition to computational drug discovery, infusing both the capabilities of learning abstract features and discovering complex molecular patterns via from data. Since biological chemical knowledge are necessary for overcoming challenges data curation, balancing, training, evaluation, it is important databases contain meaningful information regarding exact target disease each bioassay. The existing depositories...