Application of SMILES-based molecular generative model in new drug design

Kong, Weiya; Hu, Yuejuan; Zhang, Jiao; Tan, Qiaoyin

doi:10.3389/fphar.2022.1046524

Cited by 3 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SMILES strings have a wide range of applications in computer programs and databases, where they can be used for large-scale molecular analysis and processing, allowing researchers to process and analyze molecular data more efficiently. SMILES strings can also be used for visualization and editing of molecular structures, providing researchers with tools to intuitively understand and analyze molecular structures. − …”

Section: Methodsmentioning

confidence: 99%

DrugSK: A Stacked Ensemble Learning Framework for Predicting Drug Combinations of Multiple Diseases

Chen,

Gao,

et al. 2024

J. Chem. Inf. Model.

View full text Add to dashboard Cite

Section: Methodsmentioning

confidence: 99%

DrugSK: A Stacked Ensemble Learning Framework for Predicting Drug Combinations of Multiple Diseases

Chen,

Gao,

et al. 2024

J. Chem. Inf. Model.

View full text Add to dashboard Cite

“…This capacity enables RNNs to capture dynamic temporal behaviors crucial for analyzing timeseries data, genomic sequences, protein structures, and Simplified Molecular Input Line Entry System (SMILES) strings. [148] However, traditional RNNs encounter challenges in learning long-term dependencies within data sequences due to the vanishing gradient problem, which complicates the retention of information over extended sequences. [117] To surmount this limitation, advancements in RNN architecture have been introduced, including Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs).…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

“…Unlike traditional ANNs, RNNs are equipped with loops in their architecture, allowing the network to retain information from one step and pass it to the next, facilitating the processing of variable‐length input sequences. This capacity enables RNNs to capture dynamic temporal behaviors crucial for analyzing time‐series data, genomic sequences, protein structures, and Simplified Molecular Input Line Entry System (SMILES) strings [148] …”

Section: Artificial Neural Networkmentioning

confidence: 99%

The Role of AI in Drug Discovery

Abbas,

Rassam,

Karamshahi

et al. 2024

ChemBioChem

View full text Add to dashboard Cite

The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.

show abstract

“…[3] However, RNN-based models have certain drawbacks: due to the structural issues of its model itself, problems such as gradient vanishing and gradient explosion are prone to occur when facing large datasets, and the final output is highly correlated with the last few inputs. [4] So we focused on VAE-based models. The models based on VAE reduct dimensionality to learn latent representations from input data .Therefore they can learn the probability distribution of the dataset and Reproduce the input data as much as possible.…”

Section: Introductionmentioning

confidence: 99%

Molecular Generator for Multi-objective Optimization Based on the Pareto Algorithm

2023

medsc

View full text Add to dashboard Cite

Designing molecules with certain physicochemical features can promote the discovery and optimization of lead compounds. However, most molecular generation models optimize only one physicochemical property, which is not sufficient to determine the availability of a drug. This is because the availability of a drug substance molecule depends on the combined effect of many physicochemical properties. In this study, the pareto method was conducted to optimize the compounds for multi-target molecular characteristics in close approximation to those of the reference compound. In addition, we similarly used the random SMILES method involving amplification and diversification of molecules. Finally, we further examined the generation ability of the model and also analyzed the probability distribution of the physicochemical attributes and molecular structure of the created compounds. We expect that the model could develop additional molecules for exploring a bigger chemical space for medicinal chemists.

show abstract

Application of SMILES-based molecular generative model in new drug design

Cited by 3 publications

References 19 publications

DrugSK: A Stacked Ensemble Learning Framework for Predicting Drug Combinations of Multiple Diseases

DrugSK: A Stacked Ensemble Learning Framework for Predicting Drug Combinations of Multiple Diseases

The Role of AI in Drug Discovery

Molecular Generator for Multi-objective Optimization Based on the Pareto Algorithm

Contact Info

Product

Resources

About