In the field of molecular simulation for drug design, traditional molecular mechanic force fields and quantum chemical theories have been instrumental but limited in terms of scalability and computational efficiency. To overcome these limitations, ma...
IEEE/ACM transactions on computational biology and bioinformatics
Dec 25, 2023
In recent years, machine learning has gained increasing traction in the study of molecules, enabling researchers to tackle challenging tasks including molecular property prediction and drug design.Consequently, there remains an open challenge to deve...
Journal of biomolecular structure & dynamics
Dec 22, 2023
The Adenosine A receptor (AAR) is considered a novel potential target for the immunotherapy of cancer, and AAR antagonists have an inhibitory effect on tumor growth, proliferation, and metastasis. In our previous studies, we identified a class of ben...
Quantitative structure-activity relationship (QSAR) modelling, an approach that was introduced 60 years ago, is widely used in computer-aided drug design. In recent years, progress in artificial intelligence techniques, such as deep learning, the rap...
The recent and extraordinary increase in computer power, along with the availability of efficient algorithms based on artificial intelligence, has prompted a large number of inexperienced scientists to challenge the complex and yet competitive world ...
Journal of biomolecular structure & dynamics
Nov 8, 2023
Recent advances in hardware and software algorithms have led to the rise of data-driven approaches for designing therapeutic modalities. One of the major causes of human mortality is diabetes. Thus, there is a tremendous opportunity for research into...
Journal of chemical information and modeling
Nov 7, 2023
The discovery of new drugs has important implications for human health. Traditional methods for drug discovery rely on experiments to optimize the structure of lead molecules, which are time-consuming and high-cost. Recently, artificial intelligence ...
CONTEXT: With the wide application of deep learning in drug research and development, de novo molecular design methods based on recurrent neural network (RNN) have strong advantages in drug molecule generation. The RNN model can be used to learn the ...
Recent research in drug discovery dealing with many faces difficulties, including development of new drugs during disease outbreak and drug resistance due to rapidly accumulating mutations. Virtual screening is the most widely used method in computer...