539 research outputs found
Bioinformatics
This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here
Recent Developments in Structure-Based Virtual Screening Approaches
Drug development is a wide scientific field that faces many challenges these
days. Among them are extremely high development costs, long development times,
as well as a low number of new drugs that are approved each year. To solve
these problems, new and innovate technologies are needed that make the drug
discovery process of small-molecules more time and cost-efficient, and which
allow to target previously undruggable target classes such as protein-protein
interactions. Structure-based virtual screenings have become a leading
contender in this context. In this review, we give an introduction to the
foundations of structure-based virtual screenings, and survey their progress in
the past few years. We outline key principles, recent success stories, new
methods, available software, and promising future research directions. Virtual
screenings have an enormous potential for the development of new small-molecule
drugs, and are already starting to transform early-stage drug discovery.Comment: 22 pages, 2 figure
Computational structure‐based drug design: Predicting target flexibility
The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant
from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft
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Development and analysis of Tinker-OpenMM as a GPU-based free energy perturbation engine
The utilization of computational technologies for the lead optimization process is one of the biggest challenges in the computational chemistry field. In this dissertation, I describe the addition of GPU-based absolute and relative free energy calculation methods using polarizable force field AMOEBA to Tinker-OpenMM. I then proceed to test the capabilities of this platform by studying the binding free energy and binding structures of derivatives of the MELK inhibitor IN17. Also, I present the implementation of virial-based pressure control to the Tinker-OpenMM platform that is needed for performing isobaric simulations.Cellular and Molecular Biolog
Exploration of Reaction Pathways and Chemical Transformation Networks
For the investigation of chemical reaction networks, the identification of
all relevant intermediates and elementary reactions is mandatory. Many
algorithmic approaches exist that perform explorations efficiently and
automatedly. These approaches differ in their application range, the level of
completeness of the exploration, as well as the amount of heuristics and human
intervention required. Here, we describe and compare the different approaches
based on these criteria. Future directions leveraging the strengths of chemical
heuristics, human interaction, and physical rigor are discussed.Comment: 48 pages, 4 figure
On the role of metaheuristic optimization in bioinformatics
Metaheuristic algorithms are employed to solve complex and large-scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics
Advances in Human-Protein Interaction - Interactive and Immersive Molecular Simulations
International audienc
AI in drug discovery and its clinical relevance
The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of de novo design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article.
Other InformationPublished in:HeliyonLicense: https://creativecommons.org/licenses/by/4.0/See article on publisher's website: https://doi.org/10.1016/j.heliyon.2023.e17575 </p
Deep learning optimization for drug-target interaction prediction in COVID-19 using graphic processing unit
The exponentially increasing bioinformatics data raised a new problem: the computation time length. The amount of data that needs to be processed is not matched by an increase in hardware performance, so it burdens researchers on computation time, especially on drug-target interaction prediction, where the computational complexity is exponential. One of the focuses of high-performance computing research is the utilization of the graphics processing unit (GPU) to perform multiple computations in parallel. This study aims to see how well the GPU performs when used for deep learning problems to predict drug-target interactions. This study used the gold-standard data in drug-target interaction (DTI) and the coronavirus disease (COVID-19) dataset. The stages of this research are data acquisition, data preprocessing, model building, hyperparameter tuning, performance evaluation and COVID-19 dataset testing. The results of this study indicate that the use of GPU in deep learning models can speed up the training process by 100 times. In addition, the hyperparameter tuning process is also greatly helped by the presence of the GPU because it can make the process up to 55 times faster. When tested using the COVID-19 dataset, the model showed good performance with 76% accuracy, 74% F-measure and a speed-up value of 179
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