AIMC Topic: Drug Design

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Training recurrent neural networks as generative neural networks for molecular structures: how does it impact drug discovery?

Expert opinion on drug discovery
INTRODUCTION: Deep learning approaches have become popular in recent years in de novo drug design. Generative models for molecule generation and optimization have shown promising results. Molecules trained on different chemical data could regenerate ...

De novo design of anti-tuberculosis agents using a structure-based deep learning method.

Journal of molecular graphics & modelling
Mycobacterium tuberculosis (Mtb) is a pathogen of major concern due to its ability to withstand both first- and second-line antibiotics, leading to drug resistance. Thus, there is a critical need for identification of novel anti-tuberculosis agents t...

Adversarial deep evolutionary learning for drug design.

Bio Systems
The design of a new therapeutic agent is a time-consuming and expensive process. The rise of machine intelligence provides a grand opportunity of expeditiously discovering novel drug candidates through smart search in the vast molecular structural sp...

Drug Design Using Reinforcement Learning with Graph-Based Deep Generative Models.

Journal of chemical information and modeling
Machine learning provides effective computational tools for exploring the chemical space via deep generative models. Here, we propose a new reinforcement learning scheme to fine-tune graph-based deep generative models for molecular design tasks. We ...

Drug-target binding affinity prediction method based on a deep graph neural network.

Mathematical biosciences and engineering : MBE
The development of new drugs is a long and costly process, Computer-aided drug design reduces development costs while computationally shortening the new drug development cycle, in which DTA (Drug-Target binding Affinity) prediction is a key step to s...

Developing Role for Artificial Intelligence in Drug Discovery in Drug Design, Development, and Safety Assessment.

Chemical research in toxicology
Artificial intelligence (AI) is a rapidly growing discipline in the field of chemical toxicology. Herein, we provide a broad overview of research presented at the Fall 2022 American Chemical Society meeting, highlighting how AI is being applied acros...

Deep learning methods for molecular representation and property prediction.

Drug discovery today
With advances in artificial intelligence (AI) methods, computer-aided drug design (CADD) has developed rapidly in recent years. Effective molecular representation and accurate property prediction are crucial tasks in CADD workflows. In this review, w...

Roughness of Molecular Property Landscapes and Its Impact on Modellability.

Journal of chemical information and modeling
In molecular discovery and drug design, structure-property relationships and activity landscapes are often qualitatively or quantitatively analyzed to guide the navigation of chemical space. The roughness (or smoothness) of these molecular property l...

Identifying Drug Targets of Oral Squamous Cell Carcinoma through a Systems Biology Method and Genome-Wide Microarray Data for Drug Discovery by Deep Learning and Drug Design Specifications.

International journal of molecular sciences
In this study, we provide a systems biology method to investigate the carcinogenic mechanism of oral squamous cell carcinoma (OSCC) in order to identify some important biomarkers as drug targets. Further, a systematic drug discovery method with a dee...

Modified Electrostatic Complementary Score Function and Its Application Boundary Exploration in Drug Design.

Journal of chemical information and modeling
In recent years, machine learning (ML) models have been found to quickly predict various molecular properties with accuracy comparable to high-level quantum chemistry methods. One such example is the calculation of electrostatic potential (ESP). Diff...