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PaccMannRL: De novo generation of hit-like anticancer molecules from transcriptomic data via reinforcement learning.

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In this article, a hybrid VAE was used to generate drugs with high predicted efficacy against cell lines or cancer types, using an anticancer drug sensitivity prediction model as reward function.
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This article is published in iScience.The article was published on 2021-03-05 and is currently open access. It has received 38 citations till now.

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De novo molecular design with deep molecular generative models for PPI inhibitors

TL;DR: A protein-protein interaction (PPI) targeted drug-likeness dataset is constructed and a deep molecular generative framework is proposed to generate novel drug- likeness molecules from the features of the seed compounds to improve the de novo design of PPI-targeted compounds.
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Leveraging molecular structure and bioactivity with chemical language models for de novo drug design

TL;DR: In this paper , a collection of virtual molecules was created with a generative chemical language model (CLM) for de novo molecular structure generation by learning from a textual representation of molecules.
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DeepTTA: a transformer-based model for predicting cancer drug response

TL;DR: DeepTTA, a novel end-to-end deep learning model that utilizes transformer for drug representation learning and a multilayer neural network for transcriptomic data prediction of the anti-cancer drug responses, is proposed and discovered that anti- cancer drugs bortezomib and dactinomycin provide a potential therapeutic option with multiple clinical indications.
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Potent antibiotic design via guided search from antibacterial activity evaluations

Lu Chen, +2 more
- 27 Jan 2023 - 
TL;DR: Liang et al. as mentioned in this paper established a framework, MDAGS (Molecular Design via Attribute-Guided Search), to optimize and generate potent antibiotic molecules, which can generate novel compounds with desirable antibacterial activity without the need for extensive expensive and time-consuming evaluations.
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Perplexity-Based Molecule Ranking and Bias Estimation of Chemical Language Models

TL;DR: In this paper , the authors apply the perplexity metric to determine the degree to which the molecules generated by a chemical language model match the desired design objectives, which allows identifying and ranking the most promising molecular designs based on the probabilities learned by the CLM.
References
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Journal ArticleDOI

Nonlinear component analysis as a kernel eigenvalue problem

TL;DR: A new method for performing a nonlinear form of principal component analysis by the use of integral operator kernel functions is proposed and experimental results on polynomial feature extraction for pattern recognition are presented.
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The cancer genome atlas pan-cancer analysis project

John N. Weinstein, +379 more
- 01 Oct 2013 - 
TL;DR: The Pan-Cancer initiative compares the first 12 tumor types profiled by TCGA with a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages.
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DrugBank 5.0: a major update to the DrugBank database for 2018

TL;DR: This year’s update, DrugBank 5.0, represents the most significant upgrade to the database in more than 10 years and significant improvements have been made to the quantity, quality and consistency of drug indications, drug binding data as well as drug-drug and drug-food interactions.
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Extended-Connectivity Fingerprints

TL;DR: A description of their implementation has not previously been presented in the literature, and ECFPs can be very rapidly calculated and can represent an essentially infinite number of different molecular features.
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The NCI60 human tumour cell line anticancer drug screen

TL;DR: The development, use and productivity of the NCI60 screen are reviewed, highlighting several outcomes that have contributed to advances in cancer chemotherapy.
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