Feature functional theory–binding predictor (FFT–BP) for the blind prediction of binding free energies

Bao, Wang; Zhao, Zhixiong; Nguyen, Duc Duy; Wei, Guo-Wei

doi:10.1007/s00214-017-2083-1

Cited by 36 publications

(54 citation statements)

References 97 publications

(162 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These approaches have been widely used in solving QSAR prediction problems, as well as solvation and protein-ligand binding free energy predictions. 27,39,40 They naturally handle correlation between descriptors, and usually do not require a sophisticated feature selection procedure. Most importantly, both RF and GBDT are essentially insensitive to parameters and robust to redundant features.…”

Section: Ensemble Methodsmentioning

confidence: 99%

Quantitative Toxicity Prediction Using Topology Based Multitask Deep Neural Networks

Wei

2018

J. Chem. Inf. Model.

Self Cite

139

168

View full text Add to dashboard Cite

The understanding of toxicity is of paramount importance to human health and environmental protection. Quantitative toxicity analysis has become a new standard in the field. This work introduces element specific persistent homology (ESPH), an algebraic topology approach, for quantitative toxicity prediction. ESPH retains crucial chemical information during the topological abstraction of geometric complexity and provides a representation of small molecules that cannot be obtained by any other method. To investigate the representability and predictive power of ESPH for small molecules, ancillary descriptors have also been developed based on physical models. Topological and physical descriptors are paired with advanced machine learning algorithms, such as the deep neural network (DNN), random forest (RF), and gradient boosting decision tree (GBDT), to facilitate their applications to quantitative toxicity predictions. A topology based multitask strategy is proposed to take the advantage of the availability of large data sets while dealing with small data sets. Four benchmark toxicity data sets that involve quantitative measurements are used to validate the proposed approaches. Extensive numerical studies indicate that the proposed topological learning methods are able to outperform the state-of-the-art methods in the literature for quantitative toxicity analysis. Our online server for computing element-specific topological descriptors (ESTDs) is available at http://weilab.math.msu.edu/TopTox/ .

show abstract

Section: Ensemble Methodsmentioning

confidence: 99%

Quantitative Toxicity Prediction Using Topology Based Multitask Deep Neural Networks

Wei

2018

J. Chem. Inf. Model.

Self Cite

139

168

View full text Add to dashboard Cite

show abstract

“…Overall, we believe that with a better set of molecular descriptors, molecular parametrization, and molecular partition, the proposed FFT‐based solvation free energy prediction can be further improved. The application of the proposed approach to protein–ligand binding affinity prediction is reported elsewhere …”

Section: Discussionmentioning

confidence: 99%

Breaking the polar‐nonpolar division in solvation free energy prediction

Wang

et al. 2017

J Comput Chem

Self Cite

View full text Add to dashboard Cite

Implicit solvent models divide solvation free energies into polar and nonpolar additive contributions, whereas polar and nonpolar interactions are inseparable and nonadditive. We present a feature functional theory (FFT) framework to break this ad hoc division. The essential ideas of FFT are as follows: (i) representability assumption: there exists a microscopic feature vector that can uniquely characterize and distinguish one molecule from another; (ii) feature-function relationship assumption: the macroscopic features, including solvation free energy, of a molecule is a functional of microscopic feature vectors; and (iii) similarity assumption: molecules with similar microscopic features have similar macroscopic properties, such as solvation free energies. Based on these assumptions, solvation free energy prediction is carried out in the following protocol. First, we construct a molecular microscopic feature vector that is efficient in characterizing the solvation process using quantum mechanics and Poisson-Boltzmann theory. Microscopic feature vectors are combined with macroscopic features, that is, physical observable, to form extended feature vectors. Additionally, we partition a solvation dataset into queries according to molecular compositions. Moreover, for each target molecule, we adopt a machine learning algorithm for its nearest neighbor search, based on the selected microscopic feature vectors. Finally, from the extended feature vectors of obtained nearest neighbors, we construct a functional of solvation free energy, which is employed to predict the solvation free energy of the target molecule. The proposed FFT model has been extensively validated via a large dataset of 668 molecules. The leave-one-out test gives an optimal root-mean-square error (RMSE) of 1.05 kcal/mol. FFT predictions of SAMPL0, SAMPL1, SAMPL2, SAMPL3, and SAMPL4 challenge sets deliver the RMSEs of 0.61, 1.86, 1.64, 0.86, and 1.14 kcal/mol, respectively. Using a test set of 94 molecules and its associated training set, the present approach was carefully compared with a classic solvation model based on weighted solvent accessible surface area. © 2017 Wiley Periodicals, Inc.

show abstract

“…Therefore, these methods have been applied to a variety of QSAR prediction problems, such as toxicity, solvation, and binding affinity predictions. 4,27,41,43,44…”

Section: Iib Ensemble Methodsmentioning

confidence: 99%

Are 2D fingerprints still valuable for drug discovery?

Gao

Nguyen

Sresht

et al. 2020

Phys. Chem. Chem. Phys.

Self Cite

101

View full text Add to dashboard Cite

Recently, molecular fingerprints extracted from three-dimensional (3D) structures using advanced mathematics, such as algebraic topology, differential geometry, and graph theory have been paired with efficient machine learning, especially deep learning algorithms to outperform other methods in drug discovery applications and competitions. This raises the question of whether classical 2D fingerprints are still valuable in computer-aided drug discovery. This work considers 23 datasets associated with four typical problems, namely protein-ligand binding, toxicity, solubility and partition coefficient to assess the performance of eight 2D fingerprints. Advanced machine learning algorithms including random forest, gradient boosted decision tree, single-task deep neural network and multitask deep neural network are employed to construct efficient 2D-fingerprint based models. Additionally, appropriate consensus models are built to further enhance the performance of 2D-fingerprintbased methods. It is demonstrated that 2D-fingerprint-based models perform as well as the state-of-the-art 3D structure-based models for the predictions of toxicity, solubility, partition coefficient and protein-ligand binding affinity based on only ligand information. However, 3D structure-based models outperform 2D fingerprint-based methods in complex-based protein-ligand binding affinity predictions. * Corresponding to Guo-Wei Wei.Molecular fingerprints are one way of encoding the structural features of a molecule. They play a fundamental role in QSAR/QSPR analysis, virtual screening, similarity-based compound search, target molecule ranking, drug ADMET prediction, and other drug discovery processes. Molecular fingerprints are property profiles of a molecule, usually in the form of vectors with each vector element indicating the existence, the degree or the frequency of one particular structure feature. [12][13][14] Various fingerprints have been developed for molecular feature encoding in the past few decades. [15][16][17] Most fingerprints are 2D fingerprints which can be extracted from molecular connection tables without 3D structure information. However, high dimensional fingerprints have also been developed to utilize 3D molecular structure and other information. 18 There are four main categories of 2D fingerprints, namely substructure key-based fingerprints, topological or path-based fingerprints, circular fingerprints, and pharmacophore fingerprints. Substructure key-based fingerprints are bit strings representing the presence of certain substructures or fragments from a given list of structural keys in the compound. Molecular access system (MACCS) 19 is one of the most popular substructure keybased fingerprint methods. Topological or path-based fingerprints are based on analyzing all the fragments of a molecule following a (usually linear) path up to a certain number of bonds, and then hashing every one of these paths to create one fingerprint. The most prominent ones in this category are FP2, 20 Daylight 21 and electrotopological sta...

show abstract

Feature functional theory–binding predictor (FFT–BP) for the blind prediction of binding free energies

Cited by 36 publications

References 97 publications

Quantitative Toxicity Prediction Using Topology Based Multitask Deep Neural Networks

Quantitative Toxicity Prediction Using Topology Based Multitask Deep Neural Networks

Breaking the polar‐nonpolar division in solvation free energy prediction

Are 2D fingerprints still valuable for drug discovery?

Contact Info

Product

Resources

About