Structure-Based Predictions of Activity Cliffs

Husby, Jarmila; Bottegoni, Giovanni; Kufareva, Irina; Abagyan, Ruben; Cavalli, Andrea

doi:10.1021/ci500742b

Cited by 37 publications

(49 citation statements)

References 71 publications

(158 reference statements)

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“…Last but not least, it is emphasized that ACs continue to present important test cases for computational analysis and design. Although first successful predictions of ACs have been reported, both in two and three dimensions [15][16][17], the development of new computational concepts for AC prediction will also be of high interest.…”

Section: Expert Opinionmentioning

confidence: 99%

Representation and identification of activity cliffs

Bajorath

2017

Expert Opinion on Drug Discovery

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Section: Expert Opinionmentioning

confidence: 99%

Representation and identification of activity cliffs

Bajorath

2017

Expert Opinion on Drug Discovery

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“…Detection of true outliers is of particular importance; therefore, outlier separation from the main data is included in the data-washing step. The premise here is that similar structures have similar biological activity and that the multidimensional response surface to densely sampled data follows a normal distribution [32,42]. Therefore, we adopted the Pauta criterion to detect abnormal activityvalue points, defining a compound with activity value over three standard deviations (3σ) from the mean as an outlier.…”

Section: Data Cleaningmentioning

confidence: 99%

JavaDL: a Java-based Deep Learning Tool to Predict Drug Responses

Huang

Lwr

Chaudhari

et al. 2020

Preprint

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Motivation: Accurate prediction of drug response in each patient is the holy grail in personalized medicine. Recently, deep learning techniques have been witnessed with revival in a variety of areas such as image processing and genomic data analysis, and they will be useful for the coming age of big data analysis in pharmaceutical research and chemogenomic applications. This provides us an impetus to develop a novel deep learning platform to accurately and reliably predict the response of cancer to different drug treatments. Results: In this study, we describe a Java-based implementation of deep neural network (DNN) method, termed JavaDL, to predict cancer responses to drugs solely based on their chemical features. To this end, we devised a novel cost function by adding a regularization term which suppresses overfitting. We also adopted an "early stopping" strategy to further reduce overfit and improve the accuracy and robustness of our models. Currently the software has been integrated with a genetic algorithm-based variable selection approach and implemented as part of our JavaDL package. To evaluate our program, we compared it with several machine learning programs including SVM and kNN. We observed that JavaDL either significantly outperforms other methods in model building and prediction or obtains better results in handling big data analysis. Finally, JavaDL was employed to predict drug responses of several highly aggressive triple-negative breast cancer cell lines, and the results showed robust and accurate predictions with r 2 as high as 0.80. Availability: The program is freely available at https://imdlab.mdanderson.org/JavaDL/JavaDL.php.

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“…31,35,42 In this study, the availability of structure information of the 3D coordinates of DNMT1 enabled a structure-based interpretation of the activity cliffs using molecular modeling. 31,35,42 In this study, the availability of structure information of the 3D coordinates of DNMT1 enabled a structure-based interpretation of the activity cliffs using molecular modeling.…”

Section: Structural Interpretation Of Representative Activity Cliffsmentioning

confidence: 99%

Activity landscape sweeping: insights into the mechanism of inhibition and optimization of DNMT1 inhibitors

Naveja

Medina-Franco

2015

RSC Adv.

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The interest in developing inhibitors of DNA methyltransferases (IDNMT) as modifiers of epigenetic features for the treatment of several chronic diseases is rapidly increasing. Herein, we present insights of a chemoinformatic characterization of IDNMT focused on the analysis of the chemical space and structure-activity relationships (SAR) using activity landscape modeling (ALM). Analysis of the chemical space revealed two main groups of compounds whose chemical structures are associated with either cofactor analogs or non-nucleoside compounds. The ALM showed that non-nucleoside compounds have a continuous SAR while cofactor analogs have a rough SAR with several deep activity cliffs. Molecular modeling helped to explain the structural basis of the activity cliffs. The significance of the results is threefold: 1) the combined analysis of chemical space with activity landscape gave rise to a novel 'activity landscape sweeping' strategy that enabled a better structurebased interpretation of the SAR; 2) it is feasible -and advisable-to develop predictive models for nonnucleoside IDNMT studied in this work, and 3) structure-based interpretation of the SAR gave clear insights into the molecular mechanism of inhibition of novel IDNMT suggesting specific strategies to optimize the activity of leads compounds. structure-activity relationships; SAS maps, structure-activity-similarity maps.of cytosine, preferably within CpG dinucleotides. Also, as a product of the methylation mechanism, S-Adenosyl-L-homocysteine (SAH) is generated. 4 In mammals, four DNMT enzymes have been identified: DNMT1 (the most abundant, it is a maintenance methyltransferase that acts on hemimethylated DNA); DNMT3A and DNMT3B (de novo methyltransferases that are capable of generating new methylation patterns in DNA), and DNMT3L that is associated with DNMT3A and DNMT3B, enhancing their activity.

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Structure-Based Predictions of Activity Cliffs

Cited by 37 publications

References 71 publications

Representation and identification of activity cliffs

Representation and identification of activity cliffs

JavaDL: a Java-based Deep Learning Tool to Predict Drug Responses

Activity landscape sweeping: insights into the mechanism of inhibition and optimization of DNMT1 inhibitors

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