AI Medical Compendium Topic:
Ligands

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Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases.

Methods in molecular biology (Clifton, N.J.)
The discovery and development of drugs is a long and expensive process with a high attrition rate. Computational drug discovery contributes to ligand discovery and optimization, by using models that describe the properties of ligands and their intera...

Ultrahigh Throughput Protein-Ligand Docking with Deep Learning.

Methods in molecular biology (Clifton, N.J.)
Ultrahigh-throughput virtual screening (uHTVS) is an emerging field linking together classical docking techniques with high-throughput AI methods. We outline mechanistic docking models' goals and successes. We present different AI accelerated workflo...

Deep Learning Applied to Ligand-Based De Novo Drug Design.

Methods in molecular biology (Clifton, N.J.)
In the latest years, the application of deep generative models to suggest virtual compounds is becoming a new and powerful tool in drug discovery projects. The idea behind this review is to offer an updated view on de novo design approaches based on ...

Predicting Residence Time of GPCR Ligands with Machine Learning.

Methods in molecular biology (Clifton, N.J.)
Drug-target residence time, the duration of binding at a given protein target, has been shown in some protein families to be more significant for conferring efficacy than binding affinity. To carry out efficient optimization of residence time in drug...

De novo generation of dual-target ligands using adversarial training and reinforcement learning.

Briefings in bioinformatics
Artificial intelligence, such as deep generative methods, represents a promising solution to de novo design of molecules with the desired properties. However, generating new molecules with biological activities toward two specific targets remains an ...

Addressing data imbalance problems in ligand-binding site prediction using a variational autoencoder and a convolutional neural network.

Briefings in bioinformatics
Since 2015, a fast growing number of deep learning-based methods have been proposed for protein-ligand binding site prediction and many have achieved promising performance. These methods, however, neglect the imbalanced nature of binding site predict...

Machine-learning scoring functions trained on complexes dissimilar to the test set already outperform classical counterparts on a blind benchmark.

Briefings in bioinformatics
The superior performance of machine-learning scoring functions for docking has caused a series of debates on whether it is due to learning knowledge from training data that are similar in some sense to the test data. With a systematically revised met...

Forman persistent Ricci curvature (FPRC)-based machine learning models for protein-ligand binding affinity prediction.

Briefings in bioinformatics
Artificial intelligence (AI) techniques have already been gradually applied to the entire drug design process, from target discovery, lead discovery, lead optimization and preclinical development to the final three phases of clinical trials. Currentl...

The Impact of Crystallographic Data for the Development of Machine Learning Models to Predict Protein-Ligand Binding Affinity.

Current medicinal chemistry
BACKGROUND: One of the main challenges in the early stages of drug discovery is the computational assessment of protein-ligand binding affinity. Machine learning techniques can contribute to predicting this type of interaction. We may apply these tec...

Protein-ligand binding affinity prediction model based on graph attention network.

Mathematical biosciences and engineering : MBE
Estimating the binding affinity between proteins and drugs is very important in the application of structure-based drug design. Currently, applying machine learning to build the protein-ligand binding affinity prediction model, which is helpful to im...