Fast, Efficient Fragment-Based Coordinate Generation for Open Babel

Yoshikawa, N.; Hutchison, Geoffrey

doi:10.26434/chemrxiv.7791947.v1

Cited by 5 publications

(6 citation statements)

References 40 publications

(45 reference statements)

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“…To simulate binding affinity between protein and ligands, two docking tools, AutoDock Vina (version 1.1.2) 17 and RosettaCommons (version 3.11) 18-20 were used. Since AutoDock Vina only takes Protein Data Bank, Partial Charge (Q), & Atom Type (T) (PDBQT) format as input, we used OpenBabel (version 3.0.0) 21 to convert SDF to PDBQT. The entire protein is taken as the search space.…”

Section: Methodsmentioning

confidence: 99%

Potential Therapeutic Agents for COVID-19 Based on the Analysis of Protease and RNA Polymerase Docking

Chang¹,

Tung²,

Lee³

et al. 2020

Preprint

100

110

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The outbreak of novel coronavirus (COVID-19) infections occurring in 2019 is in dire need of finding potential therapeutic agents. In this study, we used molecular docking strategies to repurpose HIV protease inhibitors and nucleotide analogues for COVID-19. The evaluation was made on docking scores calculated by AutoDock Vina and RosettaCommons. Preliminary results suggested that Indinavir and Remdesivir have the best docking scores and the comparison of the docking sites of these two drugs shows a near perfect dock in the overlap region of the protein pocket. However, the active sites inferred from the proteins of SARS coronavirus are not compatible with the docking site of COVID-19, which may give rise to concern in the efficacy of drugs.

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Section: Methodsmentioning

confidence: 99%

Potential Therapeutic Agents for COVID-19 Based on the Analysis of Protease and RNA Polymerase Docking

Chang¹,

Tung²,

Lee³

et al. 2020

Preprint

100

110

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“…The B3LYP hybrid functional and the small basis set 3-21G are chosen to reduce the computational cost [43]. We only perform DFT optimisation on molecules conserving the same SMILES after molecular mechanics 3-D coordinates generation using Open Babel [44]. Likewise, we consider a DFT result as valid only if the SMILES remained identical after the geometric optimisation.…”

Section: Methodsmentioning

confidence: 99%

EvoMol: a flexible and interpretable evolutionary algorithm for unbiased de novo molecular generation

Leguy

Cauchy

Duval

et al. 2020

Preprint

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The objective of this work is to design a molecular generator capable of exploring known as well as unfamiliar areas of the chemical space. Our method must be flexible to adapt to very different problems. Therefore, it has to be able to work with or without the influence of prior data and knowledge. Moreover, regardless of the success, it should be as interpretable as possible to allow for diagnosis and improvement. We propose here a new open source generation method using an evolutionary algorithm to sequentially build molecular graphs. It is independent of starting data and can generate totally unseen compounds. To be able to search a large part of the chemical space, we define an original set of 7 generic mutations close to the atomic level. Our method achieves excellent performances and even records on the QED, penalised logP, SAscore, CLscore as well as the set of goal-directed functions defined in GuacaMol. To demonstrate its flexibility, we tackle a very different objective issued from the organic molecular materials domain. We show that EvoMol can generate sets of optimised molecules having high energy HOMO or low energy LUMO, starting only from methane. We can also set constraints on a synthesizability score and structural features. Finally, the interpretability of EvoMol allows for the visualisation of its exploration process as a chemically relevant tree.

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“…A key determinant for the success of this approach is that the near native conformations of the ligand must be included in the ligand conformer ensemble. Currently, there exist a number of ligand conformer generators, such as OMEGA, 127,128 RDKit, 129 and Open Babel 130 . These models have been shown in benchmark studies to reproduce reliable conformational ensembles of small molecules within seconds 130,131 .…”

Section: Methods For Efficient Sampling Of Ligand Binding Modes With ...mentioning

confidence: 99%

RNA–ligand molecular docking: Advances and challenges

Zhou

Jiang

Chen

2021

WIREs Comput Mol Sci

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With rapid advances in computer algorithms and hardware, fast and accurate virtual screening has led to a drastic acceleration in selecting potent small molecules as drug candidates. Computational modeling of RNA-small molecule interactions has become an indispensable tool for RNA-targeted drug discovery. The current models for RNA-ligand binding have mainly focused on the docking-and-scoring method. Accurate docking and scoring should tackle four crucial problems: (1) conformational flexibility of ligand, (2) conformational flexibility of RNA, (3) efficient sampling of binding sites and binding poses, and (4) accurate scoring of different binding modes. Moreover, compared with the problem of protein-ligand docking, predicting ligand binding to RNA, a negatively charged polymer, is further complicated by additional effects such as metal ion effects. Thermodynamic models based on physics-based and knowledge-based scoring functions have shown highly encouraging success in predicting ligand binding poses and binding affinities. Recently, kinetic models for ligand binding have further suggested that including dissociation kinetics (residence time) in ligand docking would result in improved performance in estimating in vivo drug efficacy. More recently, the rise of deep-learning approaches has led to new tools for predicting RNA-small molecule binding. In this review, we present an overview of the recently developed computational methods for RNA-ligand docking and their advantages and disadvantages.

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Fast, Efficient Fragment-Based Coordinate Generation for Open Babel

Cited by 5 publications

References 40 publications

Potential Therapeutic Agents for COVID-19 Based on the Analysis of Protease and RNA Polymerase Docking

Potential Therapeutic Agents for COVID-19 Based on the Analysis of Protease and RNA Polymerase Docking

EvoMol: a flexible and interpretable evolutionary algorithm for unbiased de novo molecular generation

RNA–ligand molecular docking: Advances and challenges

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