Chapter 5. Concepts and Applications of Conformal Prediction in Computational Drug Discovery

Author(s):  
Isidro Cortés-Ciriano ◽  
Andreas Bender
2015 ◽  
Vol 20 (11) ◽  
pp. 1328-1336 ◽  
Author(s):  
Chih-Yuan Tseng ◽  
Jack Tuszynski

2020 ◽  
Vol 26 (42) ◽  
pp. 7598-7622 ◽  
Author(s):  
Xiao Hu ◽  
Irene Maffucci ◽  
Alessandro Contini

Background: The inclusion of direct effects mediated by water during the ligandreceptor recognition is a hot-topic of modern computational chemistry applied to drug discovery and development. Docking or virtual screening with explicit hydration is still debatable, despite the successful cases that have been presented in the last years. Indeed, how to select the water molecules that will be included in the docking process or how the included waters should be treated remain open questions. Objective: In this review, we will discuss some of the most recent methods that can be used in computational drug discovery and drug development when the effect of a single water, or of a small network of interacting waters, needs to be explicitly considered. Results: Here, we analyse the software to aid the selection, or to predict the position, of water molecules that are going to be explicitly considered in later docking studies. We also present software and protocols able to efficiently treat flexible water molecules during docking, including examples of applications. Finally, we discuss methods based on molecular dynamics simulations that can be used to integrate docking studies or to reliably and efficiently compute binding energies of ligands in presence of interfacial or bridging water molecules. Conclusions: Software applications aiding the design of new drugs that exploit water molecules, either as displaceable residues or as bridges to the receptor, are constantly being developed. Although further validation is needed, workflows that explicitly consider water will probably become a standard for computational drug discovery soon.


2018 ◽  
Author(s):  
Traci Clymer ◽  
Vanessa Vargas ◽  
Eric Corcoran ◽  
Robin Kleinberg ◽  
Jakub Kostal

Chemicals are the basis of our society and economy, yet many existing chemicals are known to have unintended adverse effects on human and environmental health. Testing all existing and new chemicals on animals is both economically and ethically unfeasible. In this paper, a new in silico framework is presented that affords redesign of existing hazardous chemicals in commerce based on specific molecular initiating events in their adverse outcomes pathways. Our approach is based on a successful methodology implemented in computational drug discovery, and promises to dramatically lower costs associated with new chemical development by synergistically addressing chemical function and safety at the design stage. <br>


2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Nicolas Bosc ◽  
Francis Atkinson ◽  
Eloy Félix ◽  
Anna Gaulton ◽  
Anne Hersey ◽  
...  

Abstract In response to Krstajic’s letter to the editor concerning our published paper, we here take the opportunity to reply, to re-iterate that no errors in our work were identified, to provide further details, and to re-emphasise the outputs of our study. Moreover, we highlight that all of the data are freely available for the wider scientific community (including the aforementioned correspondent) to undertake follow-on studies and comparisons.


2012 ◽  
Vol 2 ◽  
pp. S822-S826 ◽  
Author(s):  
Arumugam Madeswaran ◽  
Muthuswamy Umamaheswari ◽  
Kuppusamy Asokkumar ◽  
Thirumalaisamy Sivashanmugam ◽  
Varadharajan Subhadradevi ◽  
...  

2021 ◽  
Author(s):  
Arash Keshavarzi Arshadi ◽  
Milad Salem ◽  
Arash Firouzbakht ◽  
Jiann Shiun Yuan

Abstract Deep learning’s automatic feature extraction has been a revolutionary addition to computational drug discovery, infusing both the capabilities of learning abstract features and discovering complex molecular patterns via learning from molecular data. Since biological and chemical knowledge is necessary for overcoming the challenges of data curation, balancing, training, and evaluation, it is important for databases to contain meaningful information regarding the exact target and disease of each bioassay. The existing depositories such as PubChem or ChMBL offer the screening data of millions of molecules against a variety of cells and targets, however, their bioassays contain complex biological information which can hinder their usage by the machine learning community. In this work, a comprehensive disease and target-based dataset are collected from PubChem in order to facilitate and accelerate molecular machine learning for better drug discovery. MolData is one the largest efforts to date for democratizing the molecular machine learning, with roughly 170 million drug screening results from 1.4 million unique molecules assigned to specific diseases and targets. It also provides 30 unique categories of targets and diseases. Correlation analysis of the MolData bioassays unveils valuable information for drug repurposing for multiple diseases including cancer, metabolic disorders, and infectious diseases. Finally, we provide a benchmark of more than 30 models trained on each category using multitask learning. MolData aims to pave the way for computational drug discovery and accelerate the advancement of molecular artificial intelligence in a practical manner. The MolData benchmark data is available at https:// github.com/Transilico/MolData as well as within the supplementary materials.


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