AI Medical Compendium Journal:
Current medicinal chemistry

Showing 21 to 30 of 30 articles

Trends in Deep Learning for Property-driven Drug Design.

Current medicinal chemistry
It is more pressing than ever to reduce the time and costs for the development of lead compounds in the pharmaceutical industry. The co-occurrence of advances in high-throughput screening and the rise of deep learning (DL) have enabled the developmen...

Advancements within Modern Machine Learning Methodology: Impacts and Prospects in Biomarker Discovery.

Current medicinal chemistry
BACKGROUND: The adoption of biomarkers as part of high-throughput, complex microarray or sequencing data has necessitated the discovery and validation of these data through machine learning. Machine learning has remained a fundamental and indispensab...

Deep Learning in the Quest for Compound Nomination for Fighting COVID-19.

Current medicinal chemistry
The current COVID-19 pandemic initiated an unprecedented response from clinicians and the scientific community in all relevant biomedical fields. It created an incredible multidimensional data-rich framework in which deep learning proved instrumental...

Artificial Intelligence for Epigenetics: Towards Personalized Medicine.

Current medicinal chemistry
Epigenetics is a field of biological sciences focused on the study of reversible, heritable changes in gene function, not due to modifications of the genomic sequence. These changes are the result of a complex cross-talk between several molecular mec...

New Perspectives on Machine Learning in Drug Discovery.

Current medicinal chemistry
Artificial intelligence methods, in particular, machine learning, has been playing a pivotal role in drug development, from structural design to the clinical trial. This approach is harnessing the impact of computer-aided drug discovery due to large ...

Deep Learning in Drug Target Interaction Prediction: Current and Future Perspectives.

Current medicinal chemistry
Drug-target Interactions (DTIs) prediction plays a central role in drug discovery. Computational methods in DTIs prediction have gained more attention because carrying out in vitro and in vivo experiments on a large scale is costly and time-consuming...

Convolutional Neural Network-based Virtual Screening.

Current medicinal chemistry
Virtual screening is an important means for lead compound discovery. The scoring function is the key to selecting hit compounds. Many scoring functions are currently available; however, there are no all-purpose scoring functions because different sco...

Machine Learning-Based Scoring Functions, Development and Applications with SAnDReS.

Current medicinal chemistry
BACKGROUND: Analysis of atomic coordinates of protein-ligand complexes can provide three-dimensional data to generate computational models to evaluate binding affinity and thermodynamic state functions. Application of machine learning techniques can ...

Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2.

Current medicinal chemistry
BACKGROUND: The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the developme...

Supervised Machine Learning Methods Applied to Predict Ligand- Binding Affinity.

Current medicinal chemistry
BACKGROUND: Calculation of ligand-binding affinity is an open problem in computational medicinal chemistry. The ability to computationally predict affinities has a beneficial impact in the early stages of drug development, since it allows a mathemati...