Transformer-Based Generative Model Accelerating the Development of Novel BRAF Inhibitors

Fragment based drug design represents a promising drug discovery paradigm complimentary to the traditional HTS based lead generation strategy. How to link fragment structures to increase compound affinity is remaining a challenge task in this paradigm. Hereby a novel deep generative model (AutoLinker) for linking fragments is developed with the potential for applying in the fragment-based lead generation scenario. The state-of-the-art transformer architecture was employed to learn the linker grammar and generate novel linker. Our results show that, given starting fragments and user customized linker constraints, our AutoLinker model can design abundant drug-like molecules fulfilling these constraints and its performance was superior to other reference models. Moreover, several examples were showcased that AutoLinker can be useful tools for carrying out drug design tasks such as fragment linking, lead optimization and scaffold hopping.

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Faculty Opinions recommendation of RAS mutations in cutaneous squamous-cell carcinomas in patients treated with BRAF inhibitors.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.13583957.14969057 ◽

2012 ◽

Author(s):

Channing Der

Keyword(s):

Squamous Cell ◽

Squamous Cell Carcinomas ◽

Braf Inhibitors ◽

Ras Mutations

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Effective Image Auto-Annotation via Discriminative Hyperplane Tree Based Generative Model

Journal of Software ◽

10.3724/sp.j.1001.2009.03380 ◽

2009 ◽

Vol 20 (9) ◽

pp. 2450-2461

Author(s):

Mei WANG ◽

Xiang-Dong ZHOU ◽

Hong-Tao XU ◽

Bai-Le SHI

Keyword(s):

Generative Model

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Proposal of a Generative Model of Event-based Representations for Grounded Language Understanding

10.21437/glu.2017-14 ◽

2017 ◽

Author(s):

Simon Brodeur ◽

Luca Celotti ◽

Jean Rouat

Keyword(s):

Generative Model ◽

Language Understanding ◽

Event Based

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Complex-Valued Variational Autoencoder: A Novel Deep Generative Model for Direct Representation of Complex Spectra

10.21437/interspeech.2020-1964 ◽

2020 ◽

Author(s):

Toru Nakashika

Keyword(s):

Generative Model ◽

Variational Autoencoder ◽

Complex Valued

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Clustering Count-based RNA Methylation Data Using a Nonparametric Generative Model

Current Bioinformatics ◽

10.2174/1574893613666180601080008 ◽

2018 ◽

Vol 14 (1) ◽

pp. 11-23 ◽

Cited By ~ 3

Author(s):

Lin Zhang ◽

Yanling He ◽

Huaizhi Wang ◽

Hui Liu ◽

Yufei Huang ◽

...

Keyword(s):

Clustering Analysis ◽

Methylation Level ◽

Optimal Number ◽

Generative Model ◽

Methylation Data ◽

Sequencing Data ◽

Number Of Clusters ◽

Rna Methylation ◽

Clustering Effect ◽

Optimal Number Of Clusters

Background: RNA methylome has been discovered as an important layer of gene regulation and can be profiled directly with count-based measurements from high-throughput sequencing data. Although the detailed regulatory circuit of the epitranscriptome remains uncharted, clustering effect in methylation status among different RNA methylation sites can be identified from transcriptome-wide RNA methylation profiles and may reflect the epitranscriptomic regulation. Count-based RNA methylation sequencing data has unique features, such as low reads coverage, which calls for novel clustering approaches. Objective: Besides the low reads coverage, it is also necessary to keep the integer property to approach clustering analysis of count-based RNA methylation sequencing data. Method: We proposed a nonparametric generative model together with its Gibbs sampling solution for clustering analysis. The proposed approach implements a beta-binomial mixture model to capture the clustering effect in methylation level with the original count-based measurements rather than an estimated continuous methylation level. Besides, it adopts a nonparametric Dirichlet process to automatically determine an optimal number of clusters so as to avoid the common model selection problem in clustering analysis. Results: When tested on the simulated system, the method demonstrated improved clustering performance over hierarchical clustering, K-means, MClust, NMF and EMclust. It also revealed on real dataset two novel RNA N6-methyladenosine (m6A) co-methylation patterns that may be induced directly by METTL14 and WTAP, which are two known regulatory components of the RNA m6A methyltransferase complex. Conclusion: Our proposed DPBBM method not only properly handles the count-based measurements of RNA methylation data from sites of very low reads coverage, but also learns an optimal number of clusters adaptively from the data analyzed. Availability: The source code and documents of DPBBM R package are freely available through the Comprehensive R Archive Network (CRAN): https://cran.r-project.org/web/packages/DPBBM/.

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