Drug Properties Prediction Based on Deep Learning

Abstract

In recent research on the formulation prediction of oral dissolving drugs, deep learning models with significantly improved performance compared to machine learning models were proposed. However, the performance degradation due to limitations of an imbalanced dataset with a small size and inefficient neural network structure has still not been resolved. Therefore, we propose new deep learning-based prediction models that maximize the prediction performance for disintegration time of oral fast disintegrating films (OFDF) and cumulative dissolution profiles of sustained-release matrix tablets (SRMT). In the case of OFDF, we use principal component analysis (PCA) to reduce the dimensionality of the dataset, thereby improving the prediction performance and reducing the training time. In the case of SRMT, the Wasserstein generative adversarial network (WGAN), a neural network-based generative model, is used to overcome the limitation of performance improvement due to the lack of experimental data. To the best of our knowledge, this is the first work that utilizes WGAN for pharmaceutical formulation prediction. Experimental results show that the proposed methods have superior performance than the existing methods for all the performance metrics considered.

Figure 4 from: Kota N, Kamaraj R, Murugaanandam S, Bharathi M, Kumar TS (2024) A data-driven approach utilizing a raw material database and machine learning tools to predict the disintegration time of orally fast-disintegrating tablet formulations. Pharmacia 71: 1-12. https://doi.org/10.3897/pharmacia.71.e122507

“…Meeting the critical quality attributes (CQAs) of a short DT (<180s) may pose challenges due to the complex relationship among active pharmaceutical ingredients (APIs), excipients, and the tablet manufacturing process (Szlek et al 2022). In recent years, the use of data-based prediction technologies such as ML and DL models to streamline the conventional drug discovery and development process has increased (Yoo et al 2022). ML is a subset of artificial intelligence (AI) that can acquire knowledge and make predictions on complex structures using extensive datasets.…”

“…DL is the predominant and extensively used ML method, proven successful in drug development and repurposing through the prediction of drug-target interactions and drug evaluations. This is because DL can extract complex features from input data (Yoo et al 2022). Additionally, DL techniques can achieve the highest accuracy in predicting the in-vitro efficacy of pharmaceutical dosage forms (Ma et al 2020).…”

“…Predicting the quality features of solid dosage forms has become a trend in various research approaches, such as explaining the disintegration process of ODTs through various ML models (Szlek et al 2022). Forecasting breaking force of tablets and DT of tablet formulations based on ML tools (Akseli et al 2017), computational intelligence for the prediction of DT of ODTs (Szlęk et al 2021), DLbased models for the prediction of DT of ODTs (Yanga et al 2019), drug properties prediction based on DL models (Yoo et al 2022), prediction of internal tablet defects using DL Convolutional Neural Networks (Ma et al 2020), DL in drug discovery (Askr et al 2023), DL-based dosage predictions for radiotherapy targeting the head and neck region (Gronberg 2023), prediction of pharmacological properties of drugs using DL models (Aliper et al 2016), quantifying the composition of amlodipine and enalapril in combination tablets with artificial neural networks (ANN) (Behei et al 2022), prediction of DT of ODTs using ANN (Hana et al 2018). These instances demonstrate that ML models can provide accurate predictions of CQAs, and it is possible to derive prediction rules from these models.…”

Figure 1 from: Kota N, Kamaraj R, Murugaanandam S, Bharathi M, Kumar TS (2024) A data-driven approach utilizing a raw material database and machine learning tools to predict the disintegration time of orally fast-disintegrating tablet formulations. Pharmacia 71: 1-12. https://doi.org/10.3897/pharmacia.71.e122507

“…Meeting the critical quality attributes (CQAs) of a short DT (<180s) may pose challenges due to the complex relationship among active pharmaceutical ingredients (APIs), excipients, and the tablet manufacturing process (Szlek et al 2022). In recent years, the use of data-based prediction technologies such as ML and DL models to streamline the conventional drug discovery and development process has increased (Yoo et al 2022). ML is a subset of artificial intelligence (AI) that can acquire knowledge and make predictions on complex structures using extensive datasets.…”

Deep Learning for Identifying Promising Drug Candidates in Drug–Phospholipid Complexes

“…In recent years, in silico approaches have gained popularity for identifying candidate drugs [ 3 , 4 ]. In particular, numerous studies have utilized machine learning (ML) and deep learning (DL) techniques to identify candidate drugs [ 5 , 6 , 7 , 8 ].…”