New drug discovery pipeline

Mittal, Pooja; Chopra, Hitesh; Kaur, Komal Preet; Gautam, Rupesh K.

doi:10.1016/b978-0-323-99137-7.00003-4

Cited by 2 publications

(1 citation statement)

References 37 publications

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“…Furthermore, in comparison to the more substantial and active preclinical biotechnology pipeline, the overall amount of spent on research and development in clinical development is insufficient to meet global health demands. As a result, the pharmaceutical industry’s R&D stream is running down of innovative drugs ( Figure 5 ) ( Mittal et al, 2023 ).…”

Section: The Race Against Antibiotic Scarcity: Time For Immediate Int...mentioning

confidence: 99%

Extensively and multidrug-resistant bacterial strains: case studies of antibiotics resistance

Almutairy

2024

Front. Microbiol.

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The development of antibiotic resistance compromises the effectiveness of our most effective defenses against bacterial infections, presenting a threat to global health. To date, a large number of research articles exist in the literature describing the case reports associated with extensively drug-resistant (XDR) and multidrug-resistant (MDR) bacterial strains. However, these findings are scattered, making it time-consuming for researchers to locate promising results and there remains a need for a comparative study to compile these case reports from various geographical regions including the Kingdom of Saudi Arabia. Additionally, no study has yet been published that compares the genetic variations and case reports of MDR and XDR strains identified from Saudi Arabia, the Middle East, Central Europe, and Asian countries. This study attempts to provide a comparative analysis of several MDR and XDR case reports from Saudi Arabia alongside other countries. Furthermore, the purpose of this work is to demonstrate the genetic variations in the genes underlying the resistance mechanisms seen in MDR and XDR bacterial strains that have been reported in Saudi Arabia and other countries. To cover the gap, this comprehensive review explores the complex trends in antibiotic resistance and the growing risk posed by superbugs. We provide context on the concerning spread of drug-resistant bacteria by analyzing the fundamental mechanisms of antibiotic resistance and looking into individual case reports. In this article, we compiled various cases and stories associated with XDR and MDR strains from Saudi Arabia and various other countries including China, Egypt, India, Poland, Pakistan, and Taiwan. This review will serve as basis for highlighting the growing threat of MDR, XDR bacterial strains in Saudi Arabia, and poses the urgent need for national action plans, stewardship programs, preventive measures, and novel antibiotics research in the Kingdom.

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Section: The Race Against Antibiotic Scarcity: Time For Immediate Int...mentioning

confidence: 99%

Extensively and multidrug-resistant bacterial strains: case studies of antibiotics resistance

Almutairy

2024

Front. Microbiol.

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GEFormerDTA: drug target affinity prediction based on transformer graph for early fusion

Liu,

Xing,

Zhang

et al. 2024

Sci Rep

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Predicting the interaction affinity between drugs and target proteins is crucial for rapid and accurate drug discovery and repositioning. Therefore, more accurate prediction of DTA has become a key area of research in the field of drug discovery and drug repositioning. However, traditional experimental methods have disadvantages such as long operation cycles, high manpower requirements, and high economic costs, making it difficult to predict specific interactions between drugs and target proteins quickly and accurately. Some methods mainly use the SMILES sequence of drugs and the primary structure of proteins as inputs, ignoring the graph information such as bond encoding, degree centrality encoding, spatial encoding of drug molecule graphs, and the structural information of proteins such as secondary structure and accessible surface area. Moreover, previous methods were based on protein sequences to learn feature representations, neglecting the completeness of information. To address the completeness of drug and protein structure information, we propose a Transformer graph-based early fusion research approach for drug-target affinity prediction (GEFormerDTA). Our method reduces prediction errors caused by insufficient feature learning. Experimental results on Davis and KIBA datasets showed a better prediction of drugtarget affinity than existing affinity prediction methods.

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New drug discovery pipeline

Cited by 2 publications

References 37 publications

Extensively and multidrug-resistant bacterial strains: case studies of antibiotics resistance

Extensively and multidrug-resistant bacterial strains: case studies of antibiotics resistance

GEFormerDTA: drug target affinity prediction based on transformer graph for early fusion

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