175,609 research outputs found
Benchmarking network propagation methods for disease gene identification
In-silico identification of potential target genes for disease is an essential aspect of drug target discovery. Recent studies suggest that successful targets can be found through by leveraging genetic, genomic and protein interaction information. Here, we systematically tested the ability of 12 varied algorithms, based on network propagation, to identify genes that have been targeted by any drug, on gene-disease data from 22 common non-cancerous diseases in OpenTargets. We considered two biological networks, six performance metrics and compared two types of input gene-disease association scores. The impact of the design factors in performance was quantified through additive explanatory models. Standard cross-validation led to over-optimistic performance estimates due to the presence of protein complexes. In order to obtain realistic estimates, we introduced two novel protein complex-aware cross-validation schemes. When seeding biological networks with known drug targets, machine learning and diffusion-based methods found around 2-4 true targets within the top 20 suggestions. Seeding the networks with genes associated to disease by genetics decreased performance below 1 true hit on average. The use of a larger network, although noisier, improved overall performance. We conclude that diffusion-based prioritisers and machine learning applied to diffusion-based features are suited for drug discovery in practice and improve over simpler neighbour-voting methods. We also demonstrate the large impact of choosing an adequate validation strategy and the definition of seed disease genesPeer ReviewedPostprint (published version
Explainable Artificial Intelligence for Drug Discovery and Development -- A Comprehensive Survey
The field of drug discovery has experienced a remarkable transformation with
the advent of artificial intelligence (AI) and machine learning (ML)
technologies. However, as these AI and ML models are becoming more complex,
there is a growing need for transparency and interpretability of the models.
Explainable Artificial Intelligence (XAI) is a novel approach that addresses
this issue and provides a more interpretable understanding of the predictions
made by machine learning models. In recent years, there has been an increasing
interest in the application of XAI techniques to drug discovery. This review
article provides a comprehensive overview of the current state-of-the-art in
XAI for drug discovery, including various XAI methods, their application in
drug discovery, and the challenges and limitations of XAI techniques in drug
discovery. The article also covers the application of XAI in drug discovery,
including target identification, compound design, and toxicity prediction.
Furthermore, the article suggests potential future research directions for the
application of XAI in drug discovery. The aim of this review article is to
provide a comprehensive understanding of the current state of XAI in drug
discovery and its potential to transform the field.Comment: 13 pages, 3 figure
AI Enabled Drug Design and Side Effect Prediction Powered by Multi-Objective Evolutionary Algorithms & Transformer Models
Due to the large search space and conflicting objectives, drug design and discovery
is a difficult problem for which new machine learning (ML) approaches are required.
Here, the problem is to invent a method by which new, therapeutically useful, compounds
can be discovered; and to simultaneously avoid compounds which will fail
clinical trials or pass unwanted effects onto the end patient. By extending current
technologies as well as adding new ones, more design criteria can be included, and
more promising novel drugs can be discovered. This work advances the field of computational
drug design by (1) developing MOEA-DT, a non-deep learning application
for multi-objective molecular optimization, which generates new molecules with high
performance in a variety of design criteria; and (2) developing SEMTL-BERT, a side
effect prediction algorithm which leverages the latest ML techniques and datasets to
accomplish its task. Experiments performed show that MOEA-DT either matches or
outperforms other similar methods, and that SEMTL-BERT can enhance predictive
ability
GGL-PPI: Geometric Graph Learning to Predict Mutation-Induced Binding Free Energy Changes
Protein-protein interactions (PPIs) are critical for various biological
processes, and understanding their dynamics is essential for decoding molecular
mechanisms and advancing fields such as cancer research and drug discovery.
Mutations in PPIs can disrupt protein binding affinity and lead to functional
changes and disease. Predicting the impact of mutations on binding affinity is
valuable but experimentally challenging. Computational methods, including
physics-based and machine learning-based approaches, have been developed to
address this challenge. Machine learning-based methods, fueled by extensive PPI
datasets such as Ab-Bind, PINT, SKEMPI, and others, have shown promise in
predicting binding affinity changes. However, accurate predictions and
generalization of these models across different datasets remain challenging.
Geometric graph learning has emerged as a powerful approach, combining graph
theory and machine learning, to capture structural features of biomolecules. We
present GGL-PPI, a novel method that integrates geometric graph learning and
machine learning to predict mutation-induced binding free energy changes.
GGL-PPI leverages atom-level graph coloring and multi-scale weighted colored
geometric subgraphs to extract informative features, demonstrating superior
performance on three validation datasets, namely AB-Bind, SKEMPI 1.0, and
SKEMPI 2.0 datasets. Evaluation on a blind test set highlights the unbiased
predictions of GGL-PPI for both direct and reverse mutations. The findings
underscore the potential of GGL-PPI in accurately predicting binding free
energy changes, contributing to our understanding of PPIs and aiding drug
design efforts
Artificial Intelligence for In Silico Clinical Trials: A Review
A clinical trial is an essential step in drug development, which is often
costly and time-consuming. In silico trials are clinical trials conducted
digitally through simulation and modeling as an alternative to traditional
clinical trials. AI-enabled in silico trials can increase the case group size
by creating virtual cohorts as controls. In addition, it also enables
automation and optimization of trial design and predicts the trial success
rate. This article systematically reviews papers under three main topics:
clinical simulation, individualized predictive modeling, and computer-aided
trial design. We focus on how machine learning (ML) may be applied in these
applications. In particular, we present the machine learning problem
formulation and available data sources for each task. We end with discussing
the challenges and opportunities of AI for in silico trials in real-world
applications
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