AIMC Topic: Drug Design

Clear Filters Showing 491 to 500 of 632 articles

Applying machine learning techniques for ADME-Tox prediction: a review.

Expert opinion on drug metabolism & toxicology
INTRODUCTION: Pharmacokinetics involves the study of absorption, distribution, metabolism, excretion and toxicity of xenobiotics (ADME-Tox). In this sense, the ADME-Tox profile of a bioactive compound can impact its efficacy and safety. Moreover, eff...

Integrative strategies in drug discovery: Harnessing genomics, deep learning, and computer-aided drug design.

Computational biology and chemistry
The development of novel drugs increasingly relies on advanced omics technologies, including genomics, transcriptomics, proteomics, and metabolomics. These approaches provide insights into genetic mutations, biomarkers, and disease pathways. However,...

De novo design and bioactivity prediction of mitotic kinesin Eg5 inhibitors using MPNN and LSTM-based transfer learning.

Computers in biology and medicine
Breast cancer, the most commonly diagnosed disease worldwide, has been linked to the overexpression of the kinesin Eg5 protein, a spindle motor protein crucial for the assembly and maintenance of the bipolar spindle during mitosis. This makes Eg5 an ...

Role of artificial intelligence in cancer drug discovery and development.

Cancer letters
The role of artificial intelligence (AI) in cancer drug discovery and development has garnered significant attention due to its potential to transform the traditionally time-consuming and expensive processes involved in bringing new therapies to mark...

Qsarna: An Online Tool for Smart Chemical Space Navigation in Drug Design.

Journal of chemical information and modeling
Drug discovery is a lengthy and resource-intensive process that requires innovative computational techniques to expedite the transition from laboratory research to life-saving medications. Here, we introduce Qsarna, a comprehensive online platform th...

Harnessing computational technologies to facilitate antibody-drug conjugate development.

Nature chemical biology
Antibody-drug conjugates (ADCs) represent a powerful therapeutic approach for the treatment of a range of cancers. They merge the toxicity of known chemical agents with the specificity of monoclonal antibodies, thereby maximizing efficacy while minim...

Multi-Target Drug Design in Alzheimer's Disease Treatment: Emerging Technologies, Advantages, Challenges, and Limitations.

Pharmacology research & perspectives
Alzheimer's disease (AD) is a complex and multifactorial neurodegenerative disorder, recognized as the most prevalent form of dementia. It is characterized by multiple pathological processes, including amyloid-beta accumulation, neurofibrillary tangl...

First report on analysis of chemical space, scaffold diversity, critical structural features of HDAC11 inhibitors.

Molecular diversity
In the histone deacetylase (HDAC) family, HDAC11 is the smallest and a single member under the class IV subtype. It is important as a drug target mainly in cancer, inflammatory and autoimmune diseases. The design and development of selective HDAC11 i...

In silico design strategies for tubulin inhibitors for the development of anticancer therapies.

Expert opinion on drug discovery
INTRODUCTION: Microtubules, composing of α, β-tubulin dimers, are important for cellular processes like proliferation and transport, thereby they become suitable targets for research in cancer. Existing candidates often exhibit off-target effects, ne...

Artificial intelligence revolution in drug discovery: A paradigm shift in pharmaceutical innovation.

International journal of pharmaceutics
Integrating artificial intelligence (AI) into drug discovery has revolutionized pharmaceutical innovation, addressing the challenges of traditional methods that are costly, time-consuming, and suffer from high failure rates. By utilizing machine lear...