The dissociation rate constant () significantly impacts the drug potency and dosing frequency. This work proposes a powerful optimization-based framework for drug design guided by . First, a comprehensive database containing 2,773 unique values is created. Based on the database, a novel generic dissociation kinetic model is developed with a mixture-of-experts architecture, enabling high-throughput predictions of with high accuracy. The developed model is then integrated with an optimization-based mathematical programming approach to design drug candidates with low . Finally, the τ-RAMD method is utilized to rigorously verify the designed potential drug candidates. In a case study, the framework successfully identified numerous new potential HSP90 inhibitor candidates, achieving a maximum 45.7% improvement in residence time (τ = 1/) compared to that of a known exceptional HSP90 inhibitor. These findings demonstrate the feasibility and effectiveness of the kinetics-guided optimization-based drug design framework in designing drug candidates with prolonged τ.
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http://dx.doi.org/10.1021/acs.jcim.4c00726 | DOI Listing |
J Biomol Struct Dyn
January 2025
Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Tryptophan catabolism is a central pathway in many cancers, serving to sustain an immunosuppressive microenvironment. The key enzymes involved in this tryptophan metabolism such as indoleamine 2,3-dioxygenase 1 (IDO1) and tryptophan 2,3-dioxygenase (TDO) are reported as promising novel targets in cancer immunotherapy. IDO1 and TDO overexpression in TNBC cells promote resistance to cell death, proliferation, invasion, and metastasis.
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January 2025
Department of Immunology and Microbiology, Scripps Research Institute, La Jolla, CA 92037, USA.
Lassa fever (LF), a viral hemorrhagic fever disease with a case fatality rate that can be over 20% among hospitalized LF patients, is endemic to many West African countries. Currently, no vaccines or therapies are specifically licensed to prevent or treat LF, hence the significance of developing therapeutics against the mammarenavirus Lassa virus (LASV), the causative agent of LF. We used in silico docking approaches to investigate the binding affinities of 2015 existing drugs to LASV proteins known to play critical roles in the formation and activity of the virus ribonucleoprotein complex (vRNP) responsible for directing replication and transcription of the viral genome.
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January 2025
Chantal BIYA International Reference Centre for Research on HIV/AIDS Prevention and Management, Yaoundé P.O. Box 3077, Cameroon.
Islatravir (ISL) is a novel antiretroviral that inhibits HIV-1 reverse transcriptase translocation. The M184V mutation, known to reduce ISL's viral susceptibility in vitro, could arise from prolonged exposure to nucleoside reverse transcriptase inhibitors (NRTI) (3TC). This study evaluated the predictive efficacy of ISL and identified potentially active antiretrovirals in combination among treatment-experienced patients in Cameroon, where NRTIs (3TC) have been the backbone of ART for decades now.
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December 2024
Scientific Research Institute for Biological Safety Problems, Ministry of Health of Kazakhstan, Almaty 080409, Kazakhstan.
The global burden of COVID-19 continues to rise, and despite significant progress in vaccine development, there remains a critical need for effective treatments for the severe inflammation and acute lung injury associated with SARS-CoV-2 infection. In this study, we explored the antiviral properties of a plant-derived complex consisting of flavonol and hydroxyorganic acid compounds. Our research focused on the ability of the flavonol and hydroxyorganic acid complex to suppress the activity of several key proteins involved in the replication and maturation of SARS-CoV-2.
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December 2024
Beijing Youcare Kechuang Pharmaceutical Technology Co., Ltd., Beijing 100176, China.
Human respiratory syncytial virus (RSV) remains a significant global health threat, particularly for vulnerable populations. Despite extensive research, effective antiviral therapies are still limited. To address this urgent need, we present AVP-GPT2, a deep-learning model that significantly outperforms its predecessor, AVP-GPT, in designing and screening antiviral peptides.
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