AIMC Topic: Drug Design

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Machine learning-guided discovery and design of non-hemolytic peptides.

Scientific reports
Reducing hurdles to clinical trials without compromising the therapeutic promises of peptide candidates becomes an essential step in peptide-based drug design. Machine-learning models are cost-effective and time-saving strategies used to predict biol...

Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design.

Journal of chemical information and modeling
One of the main challenges in drug discovery is predicting protein-ligand binding affinity. Recently, machine learning approaches have made substantial progress on this task. However, current methods of model evaluation are overly optimistic in measu...

Hybrid Harris hawks optimization with cuckoo search for drug design and discovery in chemoinformatics.

Scientific reports
One of the major drawbacks of cheminformatics is a large amount of information present in the datasets. In the majority of cases, this information contains redundant instances that affect the analysis of similarity measurements with respect to drug d...

Computational Method for Quantitative Comparison of Activity Landscapes on the Basis of Image Data.

Molecules (Basel, Switzerland)
Activity landscape (AL) models are used for visualizing and interpreting structure-activity relationships (SARs) in compound datasets. Therefore, ALs are designed to present chemical similarity and compound potency information in context. Different t...

Current methods and challenges for deep learning in drug discovery.

Drug discovery today. Technologies
Driven by rapid advances in computer hardware and publicly available datasets over the past decade, deep learning has achieved tremendous success in the transformation of many computational disciplines. These novel technologies have had considerable ...

Digital Pharmaceutical Sciences.

AAPS PharmSciTech
Artificial intelligence (AI) and machine learning, in particular, have gained significant interest in many fields, including pharmaceutical sciences. The enormous growth of data from several sources, the recent advances in various analytical tools, a...

Guidelines for Recurrent Neural Network Transfer Learning-Based Molecular Generation of Focused Libraries.

Journal of chemical information and modeling
Deep learning approaches have become popular in recent years in the field of molecular design. While a variety of different methods are available, it is still a challenge to assess and compare their performance. A particularly promising approach for...

The application of machine learning techniques to innovative antibacterial discovery and development.

Expert opinion on drug discovery
INTRODUCTION: After the initial wave of antibiotic discovery, few novel classes of antibiotics have emerged, with the latest dating back to the 1980's. Furthermore, the pace of antibiotic drug discovery is unable to keep up with the increasing preval...

Insight into potent leads for alzheimer's disease by using several artificial intelligence algorithms.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie
Several proteins including S-nitrosoglutathione reductase (GSNOR), complement Factor D, complement 3b (C3b) and Protein Kinase R-like Endoplasmic Reticulum Kinase (PERK), have been demonstrated to be involved in pathogenesis pathways for Alzheimer's ...