Persistent Path-Spectral (PPS) Based Machine Learning for Protein-Ligand Binding Affinity Prediction

Knot data analysis using multiscale Gauss link integral

In the past decade, topological data analysis has emerged as a powerful algebraic topology approach in data science. Although knot theory and related subjects are a focus of study in mathematics, their success in practical applications is quite limited due to the lack of localization and quantization. We address these challenges by introducing knot data analysis (KDA), a paradigm that incorporates curve segmentation and multiscale analysis into the Gauss link integral. The resulting multiscale Gauss link integral (mGLI) recovers the global topological properties of knots and links at an appropriate scale and offers a multiscale geometric topology approach to capture the local structures and connectivities in data. By integration with machine learning or deep learning, the proposed mGLI significantly outperforms other state-of-the-art methods across various benchmark problems in 13 intricately complex biological datasets, including protein flexibility analysis, protein-ligand interactions, human Ether-à-go-go-Related Gene potassium channel blockade screening, and quantitative toxicity assessment. Our KDA opens a research area-knot deep learning-in data science.

A review on recent advancements in pharmaceutical technology transfer of tablets from an Indian perspective

OBJECTIVE

The healthcare sector is a paramount and rapidly expanding industry in India. The pharmaceutical field in India has experienced substantial growth and transformation in recent times, making significant contributions to the global healthcare market. This comprehensive review delves into the most recent innovations in pharmaceutical technology transfer (TT), particularly in the context of tablet formulations from an Indian standpoint.

SIGNIFICANCE

The pharmaceutical sector has grappled with various challenging issues, including the escalating costs of medications and the demand for patient-friendly products.

METHODS

In this technological progress era, various cutting-edge pharmaceutical technologies, such as artificial intelligence (AI), and 3D and 4D printing, play pivotal roles in drug development. Tablets, the most promising and widely utilized dosage form worldwide, require a sophisticated approach to TT. Achieving a successful TT necessitates a dedicated team with well-defined objectives, improved documentation, and effective communication.

RESULTS

The Indian Pharmaceutical Industry (IPI) possesses the potential to make significant contributions to the global healthcare sector. Moreover, we delve into the various phases of TT, highlighting the pivotal role of formulation development and process optimization in ensuring product quality, efficiency, and cost-effectiveness along with different models of TT. Additionally, we examine the challenges associated with TT and potential solutions, as well as the initiatives of the Indian government to bolster the Indian pharmaceutical sector's position as the "Pharmacy of the World".

CONCLUSION

It is concluded that there is a need to contextualize and institutionalize the tech transfer policies for successful implementation for the benefit of the global population.

An ensemble-based approach to estimate confidence of predicted protein-ligand binding affinity values

When designing a machine learning-based scoring function, we access a limited number of protein-ligand complexes with experimentally determined binding affinity values, representing only a fraction of all possible protein-ligand complexes. Consequently, it is crucial to report a measure of confidence and quantify the uncertainty in the model's predictions during test time. Here, we adopt the conformal prediction technique to evaluate the confidence of a prediction for each member of the core set of the CASF 2016 benchmark. The conformal prediction technique requires a diverse ensemble of predictors for uncertainty estimation. To this end, we introduce ENS-Score as an ensemble predictor, which includes 30 models with different protein-ligand representation approaches and achieves Pearson's correlation of 0.842 on the core set of the CASF 2016 benchmark. Also, we comprehensively investigate the residual error of each data point to assess the normality behavior of the distribution of the residual errors and their correlation to the structural features of the ligands, such as hydrophobic interactions and halogen bonding. In the end, we provide a local host web application to facilitate the usage of ENS-Score. All codes to repeat results are provided at https://github.com/miladrayka/ENS_Score.

Enhancing Generalizability in Protein-Ligand Binding Affinity Prediction with Multimodal Contrastive Learning

Improving the generalization ability of scoring functions remains a major challenge in protein-ligand binding affinity prediction. Many machine learning methods are limited by their reliance on single-modal representations, hindering a comprehensive understanding of protein-ligand interactions. We introduce a graph-neural-network-based scoring function that utilizes a triplet contrastive learning loss to improve protein-ligand representations. In this model, three-dimensional complex representations and the fusion of two-dimensional ligand and coarse-grained pocket representations converge while distancing from decoy representations in latent space. After rigorous validation on multiple external data sets, our model exhibits commendable generalization capabilities compared to those of other deep learning-based scoring functions, marking it as a promising tool in the realm of drug discovery. In the future, our training framework can be extended to other biophysical- and biochemical-related problems such as protein-protein interaction and protein mutation prediction.