Evaluating Deep Learning models for predicting ALK-5 inhibition

Espinoza, Gabriel Zarzana; Angelo, Rafaela Molina de; Oliveira, Patrícia Rufino; Honório, Káthia Maria

doi:10.1371/journal.pone.0246126

Cited by 12 publications

(3 citation statements)

References 34 publications

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“…The RL framework uses the value of the property predicted to generate a reward or loss to drive the Generator to output molecules with the desired property. Deep neural networks have been demonstrated to be capable of performing this task [ 37 ]. In our system, the Predictor is a character-level Convolutional Neural Network (CNN).…”

Section: Methodsmentioning

confidence: 99%

Memory augmented recurrent neural networks for de-novo drug design

et al. 2022

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A recurrent neural network (RNN) is a machine learning model that learns the relationship between elements of an input series, in addition to inferring a relationship between the data input to the model and target output. Memory augmentation allows the RNN to learn the interrelationships between elements of the input over a protracted length of the input series. Inspired by the success of stack augmented RNN (StackRNN) to generate strings for various applications, we present two memory augmented RNN-based architectures: the Neural Turing Machine (NTM) and the Differentiable Neural Computer (DNC) for the de-novo generation of small molecules. We trained a character-level convolutional neural network (CNN) to predict the properties of a generated string and compute a reward or loss in a deep reinforcement learning setup to bias the Generator to produce molecules with the desired property. Further, we compare the performance of these architectures to gain insight to their relative merits in terms of the validity and novelty of the generated molecules and the degree of property bias towards the computational generation of de-novo drugs. We also compare the performance of these architectures with simpler recurrent neural networks (Vanilla RNN, LSTM, and GRU) without an external memory component to explore the impact of augmented memory in the task of de-novo generation of small molecules.

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Section: Methodsmentioning

confidence: 99%

Memory augmented recurrent neural networks for de-novo drug design

et al. 2022

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“…Machine learning (ML) based regression techniques are becoming wide spread in many areas of data analysis in the chemical 11,12 and pharmaceutical sector [13][14][15][16] ; they have recently been employed in drug development [17][18][19] , diagnostic 20 , treatment algorithm optimisation 21 , drug repurposing 2,22 and material discovery 23,24 ; however such applications are still quite limited despite being very promising 25,26 . Another application of ML technologies in drug discovery is during compound screening or hit/lead generation and optimization enabling a virtual screening platform that offers a quicker and cheaper alternative to classic testing of large compounds libraries 27,28 ; virtual screening can be generally classified in ligand-based or structure-based 28 .…”

Section: Feasibility and Application Of Machine Learning Enabled Fast...mentioning

confidence: 99%

Feasibility and application of machine learning enabled fast screening of poly-beta-amino-esters for cartilage therapies

Perni

Prokopovich

2022

Sci Rep

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Despite the large prevalence of diseases affecting cartilage (e.g. knee osteoarthritis affecting 16% of population globally), no curative treatments are available because of the limited capacity of drugs to localise in such tissue caused by low vascularisation and electrostatic repulsion. While an effective delivery system is sought, the only option is using high drug doses that can lead to systemic side effects. We introduced poly-beta-amino-esters (PBAEs) to effectively deliver drugs into cartilage tissues. PBAEs are copolymer of amines and di-acrylates further end-capped with other amine; therefore encompassing a very large research space for the identification of optimal candidates. In order to accelerate the screening of all possible PBAEs, the results of a small pool of polymers (n = 90) were used to train a variety of machine learning (ML) methods using only polymers properties available in public libraries or estimated from the chemical structure. Bagged multivariate adaptive regression splines (MARS) returned the best predictive performance and was used on the remaining (n = 3915) possible PBAEs resulting in the recognition of pivotal features; a further round of screening was carried out on PBAEs (n = 150) with small variations of structure of the main candidates from the first round. The refinements of such characteristics enabled the identification of a leading candidate predicted to improve drug uptake > 20 folds over conventional clinical treatment; this uptake improvement was also experimentally confirmed. This work highlights the potential of ML to accelerate biomaterials development by efficiently extracting information from a limited experimental dataset thus allowing patients to benefit earlier from a new technology and at a lower price. Such roadmap could also be applied for other drug/materials development where optimisation would normally be approached through combinatorial chemistry.

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“…Machine learning (ML) based regression techniques are becoming wide spread in many areas of data analysis in the chemical 11,12 and pharmaceutical sector [13][14][15][16] and have recently been employed in drug development [17][18][19] , diagnostic 20 , treatment algorithm optimisation 21 , drug repurposing 2,22 and material discovery 23,24 ; however such applications are still quite limited despite been very promising 25,26 . Figure 1 depicts how ML could be deployed to accelerate the biomaterial development process.…”

Section: Introductionmentioning

confidence: 99%

Feasibility and Application of Machine Learning Enabled Fast Screening of Poly-Beta- Amino-Esters for Cartilage Therapies

Perni

Prokopovich

2022

Preprint

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Despite the large prevalence of diseases affecting cartilage (with knee osteoarthritis affecting 16% of population globally), no curative treatments are available because of the limited capacity of drugs to localise in such tissues caused by the low vascularisation and electrostatic repulsion. While an effective delivery system is sought, the only option is using high drug doses that can lead to systemic side effects. We introduced poly beta amino esters (PBAEs) polymers to effectively deliver drugs into cartilage tissues. PBAEs are copolymer of amines and di-acrylates further end-capped with other amine, therefore encompassing a very large research space for the identification of optimal candidates. In order to accelerate the screening of all possible PBAEs, the results of a small pool of polymers (n = 90) were used to train a variety of Machine learning (ML) methods using only polymers properties available in public libraries or estimated from the chemical structure. Bagged MARS returned the best performance and was used on the remaining (n = 3915) possible PBAEs resulting in the recognition of pivotal features; further refinements of such characteristics (n = 150) enabled the identification of a leading candidate predicted to improve drug uptake > 20 folds over conventional clinical treatment. This work highlights the potential of ML to accelerate biomaterials development by efficiently extracting information from a limited experimental dataset thus allowing patients to benefit earlier from a new technology and at a lower price. Such roadmap could also be applied for other drug/materials development where optimisation would normally be approached through combinatorial chemistry.

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Evaluating Deep Learning models for predicting ALK-5 inhibition

Cited by 12 publications

References 34 publications

Memory augmented recurrent neural networks for de-novo drug design

Memory augmented recurrent neural networks for de-novo drug design

Feasibility and application of machine learning enabled fast screening of poly-beta-amino-esters for cartilage therapies

Feasibility and Application of Machine Learning Enabled Fast Screening of Poly-Beta- Amino-Esters for Cartilage Therapies

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