AIMC Topic: Drug Design

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Exploring the artificial intelligence and machine learning models in the context of drug design difficulties and future potential for the pharmaceutical sectors.

Methods (San Diego, Calif.)
Artificial intelligence (AI), particularly deep learning as a subcategory of AI, provides opportunities to accelerate and improve the process of discovering and developing new drugs. The use of AI in drug discovery is still in its early stages, but i...

Artificial intelligence in small molecule drug discovery from 2018 to 2023: Does it really work?

Bioorganic chemistry
Utilizing artificial intelligence (AI) in drug design represents an advanced approach for identifying targets and developing new drugs. Integrating AI techniques significantly reduces the workload involved in drug development and enhances the efficie...

Deep learning driven de novo drug design based on gastric proton pump structures.

Communications biology
Existing drugs often suffer in their effectiveness due to detrimental side effects, low binding affinity or pharmacokinetic problems. This may be overcome by the development of distinct compounds. Here, we exploit the rich structural basis of drug-bo...

Ligand binding affinity prediction with fusion of graph neural networks and 3D structure-based complex graph.

Physical chemistry chemical physics : PCCP
Accurate prediction of protein-ligand binding affinity is pivotal for drug design and discovery. Here, we proposed a novel deep fusion graph neural networks framework named FGNN to learn the protein-ligand interactions from the 3D structures of prote...

Artificial intelligence for natural product drug discovery.

Nature reviews. Drug discovery
Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have le...

A subcomponent-guided deep learning method for interpretable cancer drug response prediction.

PLoS computational biology
Accurate prediction of cancer drug response (CDR) is a longstanding challenge in modern oncology that underpins personalized treatment. Current computational methods implement CDR prediction by modeling responses between entire drugs and cell lines, ...

De novo drug design based on patient gene expression profiles via deep learning.

Molecular informatics
Computational de novo drug design is a challenging issue in medicine, and it is desirable to consider all of the relevant information of the biological systems in a disease state. Here, we propose a novel computational method to generate drug candida...

GENERA: A Combined Genetic/Deep-Learning Algorithm for Multiobjective Target-Oriented De Novo Design.

Journal of chemical information and modeling
This study introduces a new de novo design algorithm called that combines the capabilities of a deep-learning algorithm for automated drug-like analogue design, called , with a genetic algorithm for generating molecules with desired target-oriented ...

Improving drug discovery with a hybrid deep generative model using reinforcement learning trained on a Bayesian docking approximation.

Journal of computer-aided molecular design
Generative approaches to molecular design are an area of intense study in recent years as a method to generate new pharmaceuticals with desired properties. Often though, these types of efforts are constrained by limited experimental activity data, re...

Hit Identification Driven by Combining Artificial Intelligence and Computational Chemistry Methods: A PI5P4K-β Case Study.

Journal of chemical information and modeling
Computer-aided drug design (CADD), especially artificial intelligence-driven drug design (AIDD), is increasingly used in drug discovery. In this paper, a novel and efficient workflow for hit identification was developed within the drug discovery pla...