A New Approach for Drug Target and Bioactivity Prediction: The Multifingerprint Similarity Search Algorithm (MuSSeL)

Alberga, Domenico; Trisciuzzi, Daniela; Montaruli, Michele; Leonetti, Francesco; Mangiatordi, Giuseppe Felice

doi:10.1021/acs.jcim.8b00698

Cited by 65 publications

(65 citation statements)

References 80 publications

(120 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It should be considered as a good default method, and as a reference method for future chemogenomic benchmark studies. In particular, we did not combine sophisticated expert-crafted descriptors, as proposed in other studies with shallow algorithms [68][69][70][71], which leaves space for improvement and reinforces the interest of this approach.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of deep and shallow learning methods in chemogenomics for the prediction of drugs specificity

Playe

Stoven

2020

J Cheminform

View full text Add to dashboard Cite

Chemogenomics, also called proteochemometrics, covers a range of computational methods that can be used to predict protein-ligand interactions at large scales in the protein and chemical spaces. They differ from more classical ligand-based methods (also called QSAR) that predict ligands for a given protein receptor. In the context of drug discovery process, chemogenomics allows to tackle the question of predicting off-target proteins for drug candidates, one of the main causes of undesirable side-effects and failure within drugs development processes. The present study compares shallow and deep machine-learning approaches for chemogenomics, and explores data augmentation techniques for deep learning algorithms in chemogenomics. Shallow machine-learning algorithms rely on expertbased chemical and protein descriptors, while recent developments in deep learning algorithms enable to learn abstract numerical representations of molecular graphs and protein sequences, in order to optimise the performance of the prediction task. We first propose a formulation of chemogenomics with deep learning, called the chemogenomic neural network (CN), as a feed-forward neural network taking as input the combination of molecule and protein representations learnt by molecular graph and protein sequence encoders. We show that, on large datasets, the deep learning CN model outperforms state-of-the-art shallow methods, and competes with deep methods with expert-based descriptors. However, on small datasets, shallow methods present better prediction performance than deep learning methods. Then, we evaluate data augmentation techniques, namely multi-view and transfer learning, to improve the prediction performance of the chemogenomic neural network. We conclude that a promising research direction is to integrate heterogeneous sources of data such as auxiliary tasks for which large datasets are available, or independently, multiple molecule and protein attribute views.

show abstract

Section: Discussionmentioning

confidence: 99%

Evaluation of deep and shallow learning methods in chemogenomics for the prediction of drugs specificity

Playe

Stoven

2020

J Cheminform

View full text Add to dashboard Cite

show abstract

“…Moreover, remember that we did not fine tune this model, and most importantly, we did not considered the most relevant expert-crafted descriptors for the DTI prediction task which leaves some space for improvement and reinforces the interest of this approach. Indeed, combining multiple types of fingerprint descriptors provides the best performance [102,90,103,104] and that the best fingerprints to retrieve a protein-ligand interaction depends on the protein target [102,90]. Regarding protein descriptors, proteochemometric attributes [105] may be more suitable for DTI prediction.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of network architecture and data augmentation methods for deep learning in chemogenomics

Playe

Stoven

2019

Preprint

View full text Add to dashboard Cite

Among virtual screening methods that have been developed to facilitate the drug discovery process, chemogenomics presents the particularity to tackle the question of predicting ligands for proteins, at at scales both in the protein and chemical spaces. Therefore, in addition to to predict drug candidates for a given therapeutic protein target, like more classical ligand-based or receptor-based methods do, chemogenomics can also predict off-targets at the proteome level, and therefore, identify potential side-effects or drug repositioning opportunities. In this study, we study and compare machinelearning and deep learning approaches for chemogenomics, that are applicable to screen large sets of compounds against large sets of druggable proteins. State-of-the-art drug chemogenomics methods rely on expert-based chemical and protein descriptors or similarity measures. The recent development of deep learning approaches enabled to design algorithms that learn numerical abstract representations of molecular graphs and protein sequences in an end-to-end fashion, i.e., so that the learnt features optimise the objective function of the drug-target interaction prediction task. In this paper, we address drug-target interaction prediction at the druggable proteome-level, with what we define as the chemogenomic neuron network. This network consists of a feed-forward neuron network taking as input the combination of molecular and protein representations learnt by molecular graph and protein sequence encoders. We first propose a standard formulation of this chemogenomic neuron network. Then, we compare the performances of the standard chemogenomic network to reference deep learning or shallow (machine-learning without deep learning) methods. In particular, we show that such a representation learning approach is competitive with state-of-the-art chemogenomics with shallow methods, but not ultimately superior. We evaluate the most promising neuron network architectures and data augmentation techniques, such as multi-view and transfer learning, to improve the prediction performance of the chemogenomic network. Our results shed new insights on the design of chemogenomics approaches based on representation learning algorithms. Most importantly, we conclude from our observations that a promising research direction is to integrate heterogeneous sources of data such as various bioactivity datasets, or independently, multiple molecule and protein attribute views, instead of focusing on sophisticated, yet intuitively relevant, encoder's neuron network architecture.

show abstract

“…This is the reason why machine learning (ML) methods have recently gained such great popularity in the field of drug design. They are used both to select potential drug candidates from large compounds databases, but also to generate the structures of new chemical compounds de novo-or to optimize their physicochemical and pharmacokinetic properties [14][15][16][17][18][19][20][21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

How Sure Can We Be about ML Methods-Based Evaluation of Compound Activity: Incorporation of Information about Prediction Uncertainty Using Deep Learning Techniques

2020

View full text Add to dashboard Cite

A great variety of computational approaches support drug design processes, helping in selection of new potentially active compounds, and optimization of their physicochemical and ADMET properties. Machine learning is a group of methods that are able to evaluate in relatively short time enormous amounts of data. However, the quality of machine-learning-based prediction depends on the data supplied for model training. In this study, we used deep neural networks for the task of compound activity prediction and developed dropout-based approaches for estimating prediction uncertainty. Several types of analyses were performed: the relationships between the prediction error, similarity to the training set, prediction uncertainty, number and standard deviation of activity values were examined. It was tested whether incorporation of information about prediction uncertainty influences compounds ranking based on predicted activity and prediction uncertainty was used to search for the potential errors in the ChEMBL database. The obtained outcome indicates that incorporation of information about uncertainty of compound activity prediction can be of great help during virtual screening experiments.

show abstract

A New Approach for Drug Target and Bioactivity Prediction: The Multifingerprint Similarity Search Algorithm (MuSSeL)

Cited by 65 publications

References 80 publications

Evaluation of deep and shallow learning methods in chemogenomics for the prediction of drugs specificity

Evaluation of deep and shallow learning methods in chemogenomics for the prediction of drugs specificity

Evaluation of network architecture and data augmentation methods for deep learning in chemogenomics

How Sure Can We Be about ML Methods-Based Evaluation of Compound Activity: Incorporation of Information about Prediction Uncertainty Using Deep Learning Techniques

Contact Info

Product

Resources

About