scholarly journals Generative Model for Proposing Drug Candidates Satisfying Anticancer Properties Using a Conditional Variational Autoencoder

ACS Omega ◽  
2020 ◽  
Vol 5 (30) ◽  
pp. 18642-18650 ◽  
Author(s):  
Sunghoon Joo ◽  
Min Soo Kim ◽  
Jaeho Yang ◽  
Jeahyun Park
Keyword(s):  
Generative Model ◽  
Drug Candidates ◽  
Anticancer Properties ◽  
Variational Autoencoder

scholarly journals New Potential Drug Candidates Against SARS-CoV-2 Using Generative Model

2020 ◽  
Author(s):  
Madhusudan Verma
Keyword(s):  
Reinforcement Learning ◽  
Drug Design ◽  
Generative Model ◽  
Potential Drug ◽  
Q Learning ◽  
Learning Network ◽  
Drug Candidates ◽  
Lead Compounds ◽  
Variational Autoencoder

Based on a recently solved structure (PDB ID: 6LU7), we developed a novel advanced deep Q-learning network with the fragment-based drug design (ADQN-FBDD) along with variational autoencoder with KL annealing and circular annealing for generating potential lead compounds targeting SARS-CoV-2 3CLpro . Structure-based optimization policy (SBOP) is used in reinforcement learning. The reason for using variational autoencoders is that it does not deviate much from actual inhibitors, but since VAE suffers from KL diminishing we have used KL annealing and circular annealing to address this issue. Researchers can use this compound as potential drugs against SARS-CoV-2.

scholarly journals Machine learning augmented predictive and generative model for rupture life in ferritic and austenitic steels

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Osman Mamun ◽  
Madison Wenzlick ◽  
Arun Sathanur ◽  
Jeffrey Hawk ◽  
Ram Devanathan
Keyword(s):  
Pearson Correlation ◽  
Rupture Life ◽  
Model Performance ◽  
Austenitic Stainless Steels ◽  
Generative Model ◽  
Austenitic Steels ◽  
Gradient Boosting ◽  
Variational Autoencoder ◽  
Feature Importance ◽  
Boosting Algorithm

AbstractThe Larson–Miller parameter (LMP) offers an efficient and fast scheme to estimate the creep rupture life of alloy materials for high-temperature applications; however, poor generalizability and dependence on the constant C often result in sub-optimal performance. In this work, we show that the direct rupture life parameterization without intermediate LMP parameterization, using a gradient boosting algorithm, can be used to train ML models for very accurate prediction of rupture life in a variety of alloys (Pearson correlation coefficient >0.9 for 9–12% Cr and >0.8 for austenitic stainless steels). In addition, the Shapley value was used to quantify feature importance, making the model interpretable by identifying the effect of various features on the model performance. Finally, a variational autoencoder-based generative model was built by conditioning on the experimental dataset to sample hypothetical synthetic candidate alloys from the learnt joint distribution not existing in both 9–12% Cr ferritic–martensitic alloys and austenitic stainless steel datasets.

scholarly journals AI-Sketcher : A Deep Generative Model for Producing High-Quality Sketches

2019 ◽  
Vol 33 ◽  
pp. 2564-2571 ◽  
Author(s):  
Nan Cao ◽  
Xin Yan ◽  
Yang Shi ◽  
Chaoran Chen
Keyword(s):  
Artificial Intelligence ◽  
Large Scale ◽  
State Of The Art ◽  
Positional Information ◽  
Generative Model ◽  
Training Data ◽  
Creative Design ◽  
High Quality ◽  
User Input ◽  
Variational Autoencoder

Sketch drawings play an important role in assisting humans in communication and creative design since ancient period. This situation has motivated the development of artificial intelligence (AI) techniques for automatically generating sketches based on user input. Sketch-RNN, a sequence-to-sequence variational autoencoder (VAE) model, was developed for this purpose and known as a state-of-the-art technique. However, it suffers from limitations, including the generation of lowquality results and its incapability to support multi-class generations. To address these issues, we introduced AI-Sketcher, a deep generative model for generating high-quality multiclass sketches. Our model improves drawing quality by employing a CNN-based autoencoder to capture the positional information of each stroke at the pixel level. It also introduces an influence layer to more precisely guide the generation of each stroke by directly referring to the training data. To support multi-class sketch generation, we provided a conditional vector that can help differentiate sketches under various classes. The proposed technique was evaluated based on two large-scale sketch datasets, and results demonstrated its power in generating high-quality sketches.

A Variational Autoencoder Based Generative Model of Urban Human Mobility

2019 ◽  
Author(s):  
Dou Huang ◽  
Xuan Song ◽  
Zipei Fan ◽  
Renhe Jiang ◽  
Ryosuke Shibasaki ◽  
...  
Keyword(s):  
Human Mobility ◽  
Generative Model ◽  
Variational Autoencoder

Supervised Determined Source Separation with Multichannel Variational Autoencoder

2019 ◽  
Vol 31 (9) ◽  
pp. 1891-1914 ◽  
Author(s):  
Hirokazu Kameoka ◽  
Li Li ◽  
Shota Inoue ◽  
Shoji Makino
Keyword(s):  
Source Separation ◽  
Generative Model ◽  
Separation Technique ◽  
Separation Performance ◽  
Unknown Parameters ◽  
Baseline Method ◽  
Latent Space ◽  
Variational Autoencoder ◽  
Training Examples ◽  
Class Labels

This letter proposes a multichannel source separation technique, the multichannel variational autoencoder (MVAE) method, which uses a conditional VAE (CVAE) to model and estimate the power spectrograms of the sources in a mixture. By training the CVAE using the spectrograms of training examples with source-class labels, we can use the trained decoder distribution as a universal generative model capable of generating spectrograms conditioned on a specified class index. By treating the latent space variables and the class index as the unknown parameters of this generative model, we can develop a convergence-guaranteed algorithm for supervised determined source separation that consists of iteratively estimating the power spectrograms of the underlying sources, as well as the separation matrices. In experimental evaluations, our MVAE produced better separation performance than a baseline method.

scholarly journals Improved Anticancer Activities of a New Pentafluorothio-Substituted Vorinostat-Type Histone Deacetylase Inhibitor

2021 ◽  
Vol 14 (12) ◽  
pp. 1319
Author(s):  
Nils Goehringer ◽  
Yayi Peng ◽  
Bianca Nitzsche ◽  
Hannah Biermann ◽  
Rohan Pradhan ◽  
...  
Keyword(s):  
Anticancer Drugs ◽  
Histone Deacetylases ◽  
Parent Compound ◽  
Hdac Inhibitors ◽  
Drug Candidate ◽  
Anticancer Activities ◽  
Drug Candidates ◽  
Anticancer Properties ◽  
Ic50 Values ◽  
Potent Drug

The development of new anticancer drugs is necessary in order deal with the disease and with the drawbacks of currently applied drugs. Epigenetic dysregulations are a central hallmark of cancerogenesis and histone deacetylases (HDACs) emerged as promising anticancer targets. HDAC inhibitors are promising epigenetic anticancer drugs and new HDAC inhibitors are sought for in order to obtain potent drug candidates. The new HDAC inhibitor SF5-SAHA was synthesized and analyzed for its anticancer properties. The new compound SF5-SAHA showed strong inhibition of tumor cell growth with IC50 values similar to or lower than that of the clinically applied reference compound vorinostat/SAHA (suberoylanilide hydroxamic acid). Target specific HDAC inhibition was demonstrated by Western blot analyses. Unspecific cytotoxic effects were not observed in LDH-release measurements. Pro-apoptotic formation of reactive oxygen species (ROS) and caspase-3 activity induction in prostate carcinoma and hepatocellular carcinoma cell lines DU145 and Hep-G2 seem to be further aspects of the mode of action. Antiangiogenic activity of SF5-SAHA was observed on chorioallantoic membranes of fertilized chicken eggs (CAM assay). The presence of the pentafluorothio-substituent of SF5-SAHA increased the antiproliferative effects in both solid tumor and leukemia/lymphoma cell models when compared with its parent compound vorinostat. Based on this preliminary study, SF5-SAHA has the prerequisites to be further developed as a new HDAC inhibitory anticancer drug candidate.

Replicating Neuroscience Observations on ML/MF and AM Face Patches by Deep Generative Model

2019 ◽  
Vol 31 (12) ◽  
pp. 2348-2367
Author(s):  
Tian Han ◽  
Xianglei Xing ◽  
Jiawen Wu ◽  
Ying Nian Wu
Keyword(s):  
Linear Relationship ◽  
Latent Variables ◽  
Generative Model ◽  
Appearance Model ◽  
Shape Model ◽  
Face Images ◽  
Variational Autoencoder ◽  
The Face ◽  
Face Stimuli ◽  
Strong Linear Relationship

A recent Cell paper (Chang & Tsao, 2017 ) reports an interesting discovery. For the face stimuli generated by a pretrained active appearance model (AAM), the responses of neurons in the areas of the primate brain that are responsible for face recognition exhibit a strong linear relationship with the shape variables and appearance variables of the AAM that generates the face stimuli. In this letter, we show that this behavior can be replicated by a deep generative model, the generator network, that assumes that the observed signals are generated by latent random variables via a top-down convolutional neural network. Specifically, we learn the generator network from the face images generated by a pretrained AAM model using a variational autoencoder, and we show that the inferred latent variables of the learned generator network have a strong linear relationship with the shape and appearance variables of the AAM model that generates the face images. Unlike the AAM model, which has an explicit shape model where the shape variables generate the control points or landmarks, the generator network has no such shape model and shape variables. Yet it can learn the shape knowledge in the sense that some of the latent variables of the learned generator network capture the shape variations in the face images generated by AAM.

Sign in / Sign up

Export Citation Format

Share Document