Spresso: an ultrafast compound pre-screening method based on compound decomposition

Yanagisawa, Keisuke; Komine, S.; Suzuki, Shogo D.; Ohue, Masahito; Ishida, Takashi; Akiyama, Yutaka

doi:10.1093/bioinformatics/btx178

Cited by 13 publications

(20 citation statements)

References 32 publications

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“…To achieve these requirements, we first decomposed 1,615 FDA-approved drug structures obtained from the ZINC database 20 on 11 April 2019 with a modified Spresso decomposition algorithm 21 that cleaves rotatable bonds to generate rigid substructures. There is only one modification from the original decomposition method that does not increase a hydrogen-bond donor atom: the cleaved site is methylated if it is a hetero atom, and a hydrogen atom is added otherwise.…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

EXPRORER: Rational Cosolvent Set Construction Method for Cosolvent Molecular Dynamics Using Large-Scale Computation

Yanagisawa

Moriwaki

Terada

et al. 2021

J. Chem. Inf. Model.

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Cosolvent molecular dynamics (CMD) simulations involve an MD simulation of a protein in the presence of explicit water molecules mixed with cosolvent molecules to perform hotspot detection, binding site identification, and binding energy estimation, while other existing methods (e.g., MixMD, SILCS, and MDmix) utilize small molecules that represent functional groups of compounds. However, the cosolvent selections employed in these methods differ and there are only a few cosolvents that are commonly used in these methods. In this study, we proposed a systematic method for constructing a set of cosolvents for drug discovery, termed the EXtended PRObes set construction by REpresentative Retrieval (EXPRORER). First, we extracted typical substructures from FDA-approved drugs, generated 138 cosolvent structures, and for each cosolvent molecule, we conducted CMD simulations to generate a spatial probability distribution map of cosolvent atoms (PMAP). Analyses of PMAP similarity revealed that a cosolvent pair with a PMAP similarity greater than 0.70−0.75 shared similar structural features. We present a method for the construction of a cosolvent subset that satisfies a similarity threshold for all cosolvents, and we tested the constructed sets for four proteins. To our knowledge, this is the first study to include a systematic proposal for cosolvent set construction, and thus, the EXPRORER cosolvents will provide deeper insights into ligand binding sites of various proteins.

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Section: ■ Materials and Methodsmentioning

confidence: 99%

EXPRORER: Rational Cosolvent Set Construction Method for Cosolvent Molecular Dynamics Using Large-Scale Computation

Yanagisawa

Moriwaki

Terada

et al. 2021

J. Chem. Inf. Model.

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“…Recently, fragment-based drug discovery (FBDD) has attracted much attention in the field of drug discovery. The method evaluates a drug candidate compound by the combination of fragments of the compound [15,24,25]. The number of unique fragments included is rather saturating, even when the size of the compound database grows very rapidly [24].…”

Section: Possibility Of Application For Drug Discoverymentioning

confidence: 99%

“…The method evaluates a drug candidate compound by the combination of fragments of the compound [15,24,25]. The number of unique fragments included is rather saturating, even when the size of the compound database grows very rapidly [24]. Considering this context, several methods have been proposed for solving the problems appearing in the FBDD as combinatorial optimization problems.…”

Section: Possibility Of Application For Drug Discoverymentioning

confidence: 99%

“…First, the variety of possible fragments corresponding to the types of polycubes is enormous. For example, Yanagisawa et al [24] demonstrated that the total number of types of fragments reached 263,319 for compounds registered in the ZINC12 database [26], a collection of commercially available chemicals. Therefore, instead of dealing with all these chemically observable fragments, it is necessary to select a set of core and important fragments that can be used to navigate the early stages of drug design.…”

Section: Possibility Of Application For Drug Discoverymentioning

confidence: 99%

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Solving Generalized Polyomino Puzzles Using the Ising Model

Takabatake

Yanagisawa

Akiyama

2022

Entropy

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In the polyomino puzzle, the aim is to fill a finite space using several polyomino pieces with no overlaps or blanks. Because it is an NP-complete combinatorial optimization problem, various probabilistic and approximated approaches have been applied to find solutions. Several previous studies embedded the polyomino puzzle in a QUBO problem, where the original objective function and constraints are transformed into the Hamiltonian function of the simulated Ising model. A solution to the puzzle is obtained by searching for a ground state of Hamiltonian by simulating the dynamics of the multiple-spin system. However, previous methods could solve only tiny polyomino puzzles considering a few combinations because their Hamiltonian designs were not efficient. We propose an improved Hamiltonian design that introduces new constraints and guiding terms to weakly encourage favorable spins and pairs in the early stages of computation. The proposed model solves the pentomino puzzle represented by approximately 2000 spins with >90% probability. Additionally, we extended the method to a generalized problem where each polyomino piece could be used zero or more times and solved it with approximately 100% probability. The proposed method also appeared to be effective for the 3D polycube puzzle, which is similar to applications in fragment-based drug discovery.

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“…To overcome this limitation, computational approaches that use ligand docking scores as approximations of experimental data have been suggested. Recently, virtual screening [ 13 , 14 , 15 , 16 , 17 ] algorithms combining ML algorithms with docking scores have been suggested. Gentile et al [ 13 ] suggested a deep-docking approach that uses a docking score prediction model to reduce the resources required for docking computations.…”

Section: Introductionmentioning

confidence: 99%

V-Dock: Fast Generation of Novel Drug-like Molecules Using Machine-Learning-Based Docking Score and Molecular Optimization

Choi

Lee

2021

IJMS

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We propose a computational workflow to design novel drug-like molecules by combining the global optimization of molecular properties and protein-ligand docking with machine learning. However, most existing methods depend heavily on experimental data, and many targets do not have sufficient data to train reliable activity prediction models. To overcome this limitation, protein-ligand docking calculations must be performed using the limited data available. Such docking calculations during molecular generation require considerable computational time, preventing extensive exploration of the chemical space. To address this problem, we trained a machine-learning-based model that predicted the docking energy using SMILES to accelerate the molecular generation process. Docking scores could be accurately predicted using only a SMILES string. We combined this docking score prediction model with the global molecular property optimization approach, MolFinder, to find novel molecules exhibiting the desired properties with high values of predicted docking scores. We named this design approach V-dock. Using V-dock, we efficiently generated many novel molecules with high docking scores for a target protein, a similarity to the reference molecule, and desirable drug-like and bespoke properties, such as QED. The predicted docking scores of the generated molecules were verified by correlating them with the actual docking scores.

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Spresso: an ultrafast compound pre-screening method based on compound decomposition

Cited by 13 publications

References 32 publications

EXPRORER: Rational Cosolvent Set Construction Method for Cosolvent Molecular Dynamics Using Large-Scale Computation

EXPRORER: Rational Cosolvent Set Construction Method for Cosolvent Molecular Dynamics Using Large-Scale Computation

Solving Generalized Polyomino Puzzles Using the Ising Model

V-Dock: Fast Generation of Novel Drug-like Molecules Using Machine-Learning-Based Docking Score and Molecular Optimization

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