Machine Learning-Enabled Pipeline for Large-Scale Virtual Drug Screening

Gupta, Aayush; Zhou, Huan‐Xiang

doi:10.1021/acs.jcim.1c00710

Cited by 29 publications

(23 citation statements)

References 50 publications

(112 reference statements)

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“…As expected, the ANI geometries are very consistent with the ωB97X/6-31G* during the geometry optimization process, although there is a possible space for improvements. The ANI-2xt model showed very similar performance to ANI-2x in terms of geometry optimization, which has been widely adopted in different applications and showed reliable performance [51][52][53] . Therefore, ANI-2xt could be also used as a reliable optimizing potential in Auto3D.…”

Section: Geometry Optimizationmentioning

confidence: 90%

Auto3D: Automatic Generation of the Low-energy 3D Structures with ANI Neural Network Potentials

Liu

Zubatiuk

Roitberg

et al. 2022

Preprint

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Computational programs accelerate the chemical discovery processes but often need proper 3-dimensional molecular information as part of the input. Getting optimal molecular structures is challenging because it requires enumerating and optimizing a huge space of stereoisomers and conformers. We developed the python-based Auto3D package for generating the low-energy 3D structures using SMILES as the input. Auto3D is based on state-of-the-art algorithms and can automize the isomer enumeration and duplicate filtering process, 3D building process, geometry optimization and ranking process. Tested on 50 molecules with multiple unspecified stereocenters, Auto3D is guaranteed to find the stereo-configuration that yields the lowest-energy conformer. With Auto3D we provide an extension of the ANI model. The new model, dubbed ANI-2xt, is trained on a tautomer-rich dataset. ANI-2xt is benchmarked with DFT methods on geometry optimization, electronic and Gibbs free energy calculations. Compared with ANI-2x, ANI-2xt provides a 42% error reduction for tautomeric reaction energy calculations when using the gold-standard coupled-cluster calculation as the reference. ANI-2xt can accurately predict the energies with a ~10^6 factor speedup compared to DFT.

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Section: Geometry Optimizationmentioning

confidence: 90%

Auto3D: Automatic Generation of the Low-energy 3D Structures with ANI Neural Network Potentials

Liu

Zubatiuk

Roitberg

et al. 2022

Preprint

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“…In the current study, molecular docking, NIB screening, ML-based screening, in silico pharmacokinetics, and toxicity assessments followed by MD simulation and binding free energy calculation using the MM-GBSA method were used to explore the promising new chemical entities as CYP3A5 modulators for therapeutic applications in cardiovascular diseases. All of the above approaches have already been proven to be effective and pioneering methodologies for the identification of lead-like molecules [ 4 , 9 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ]. List of tools, approaches, and their purposes used in the current study are given in Table S1 (Supplementary File) .…”

Section: Methodsmentioning

confidence: 99%

Identification of Potential Cytochrome P450 3A5 Inhibitors: An Extensive Virtual Screening through Molecular Docking, Negative Image-Based Screening, Machine Learning and Molecular Dynamics Simulation Studies

Islam¹,

Dudekula²,

Rallabandi³

et al. 2022

IJMS

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Cytochrome P450 3A5 (CYP3A5) is one of the crucial CYP family members and has already proven to be an important drug target for cardiovascular diseases. In the current study, the PubChem database was screened through molecular docking and high-affinity molecules were adopted for further assessment. A negative image-based (NIB) model was used for a similarity search by considering the complementary shape and electrostatics of the target and small molecules. Further, the molecules were segregated into active and inactive groups through six machine learning (ML) matrices. The active molecules found in each ML model were used for in silico pharmacokinetics and toxicity assessments. A total of five molecules followed the acceptable pharmacokinetics and toxicity profiles. Several potential binding interactions between the proposed molecules and CYP3A5 were observed. The dynamic behavior of the selected molecules in the CYP3A5 was explored through a molecular dynamics (MD) simulation study. Several parameters obtained from the MD simulation trajectory explained the stability of the protein–ligand complexes in dynamic states. The high binding affinity of each molecule was revealed by the binding free energy calculation through the MM-GBSA methods. Therefore, it can be concluded that the proposed molecules might be potential CYP3A5 molecules for therapeutic application in cardiovascular diseases subjected to in vitro/in vivo validations.

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“…Interestingly, thiolutin was recently found in a model of oesophageal carcinoma to suppress motility and stemness and to increase sensitivity to cisplatin both in vitro and in vivo, by impairing the interaction between POH1 and SNAIL, a substrate of the proteasome with a role in EMT [ 64 ]. New methods of drug discovery based on large-scale virtual screening and classification by neural networks are increasingly applied, and by molecular simulations, eight structures of Rpn11 inhibitors have been recently selected [ 95 ] (Fig. 4 ).…”

Section: Pharmacological Targeting Of Poh1mentioning

confidence: 99%

“…The progress in studies on the structure of the proteasome and on the conformational states of the various subunits [ 18 ] and the introduction of new technologies for drug screening such as virtual screening will certainly help in the identification of more potent and specific inhibitors in the future. Very recently, at least eight new structures of potential POH1 inhibitors have been reported by such an approach [ 95 ].…”

Section: Conclusion Open Questions and Future Perspectivesmentioning

confidence: 99%

POH1/Rpn11/PSMD14: a journey from basic research in fission yeast to a prognostic marker and a druggable target in cancer cells

Spataro

Buetti‐Dinh

2022

Br J Cancer

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POH1/Rpn11/PSMD14 is a highly conserved protein in eukaryotes from unicellular organisms to human and has a crucial role in cellular homoeostasis. It is a subunit of the regulatory particle of the proteasome, where it acts as an intrinsic deubiquitinase removing polyubiquitin chains from substrate proteins. This function is not only coupled to the translocation of substrates into the core of the proteasome and their subsequent degradation but also, in some instances, to the stabilisation of ubiquitinated proteins through their deubiquitination. POH1 was initially discovered as a functional homologue of the fission yeast gene pad1+, which confers drug resistance when overexpressed. In translational studies, expression of POH1 has been found to be increased in several tumour types relative to normal adjacent tissue and to correlate with tumour progression, higher tumour grade, decreased sensitivity to cytotoxic drugs and poor prognosis. Proteasome inhibitors targeting the core particle of the proteasome are highly active in the treatment of myeloma, and recently developed POH1 inhibitors, such as capzimin and thiolutin, have shown promising anticancer activity in cell lines of solid tumours and leukaemia. Here we give an overview of POH1 function in the cell, of its potential role in oncogenesis and of recent progress in developing POH1-targeting drugs.

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Machine Learning-Enabled Pipeline for Large-Scale Virtual Drug Screening

Cited by 29 publications

References 50 publications

Auto3D: Automatic Generation of the Low-energy 3D Structures with ANI Neural Network Potentials

Auto3D: Automatic Generation of the Low-energy 3D Structures with ANI Neural Network Potentials

Identification of Potential Cytochrome P450 3A5 Inhibitors: An Extensive Virtual Screening through Molecular Docking, Negative Image-Based Screening, Machine Learning and Molecular Dynamics Simulation Studies

POH1/Rpn11/PSMD14: a journey from basic research in fission yeast to a prognostic marker and a druggable target in cancer cells

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