Neural multi-task learning in drug design

Allenspach, Stephan; Hiss, Jan A.; Schneider, Gisbert

doi:10.1038/s42256-023-00785-4

Cited by 9 publications

(3 citation statements)

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“…Regularization models can be integrated into the learning process to minimize bias, for example by penalizing predictions that reinforce stereotypes or inequalities [295,296]. Multi-task learning, which involves training models on multiple tasks simultaneously, can improve their ability to generalize and reduce single-task-specific biases [297][298][299]. It is also essential to make rigorous tests on various samples in order to evaluate performance and detect biases in the model's predictions.…”

Section: Techniques For Mitigating Ethical Risksmentioning

confidence: 99%

Recent Advances in Large Language Models for Healthcare

Nassiri,

Akhloufi

2024

BioMedInformatics

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Recent advances in the field of large language models (LLMs) underline their high potential for applications in a variety of sectors. Their use in healthcare, in particular, holds out promising prospects for improving medical practices. As we highlight in this paper, LLMs have demonstrated remarkable capabilities in language understanding and generation that could indeed be put to good use in the medical field. We also present the main architectures of these models, such as GPT, Bloom, or LLaMA, composed of billions of parameters. We then examine recent trends in the medical datasets used to train these models. We classify them according to different criteria, such as size, source, or subject (patient records, scientific articles, etc.). We mention that LLMs could help improve patient care, accelerate medical research, and optimize the efficiency of healthcare systems such as assisted diagnosis. We also highlight several technical and ethical issues that need to be resolved before LLMs can be used extensively in the medical field. Consequently, we propose a discussion of the capabilities offered by new generations of linguistic models and their limitations when deployed in a domain such as healthcare.

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Section: Techniques For Mitigating Ethical Risksmentioning

confidence: 99%

Recent Advances in Large Language Models for Healthcare

Nassiri,

Akhloufi

2024

BioMedInformatics

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“…DL models need large datasets to effectively learn complex patterns to generalize well to unseen data. To address this limitation, emerging concepts like transfer learning and multi-task learning have shown promise ( Allenspach et al, 2024 ). Transfer learning , in particular, facilitates domain adaptation by leveraging knowledge gained from pre-training on extensive unlabelled public datasets, thereby enhancing generalization to novel compounds.…”

Section: Background On Cell Painting Molecular Representations and Ar...mentioning

confidence: 99%

Unleashing the potential of cell painting assays for compound activities and hazards prediction

Odje,

Meijer,

von Coburg

et al. 2024

Front. Toxicol.

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The cell painting (CP) assay has emerged as a potent imaging-based high-throughput phenotypic profiling (HTPP) tool that provides comprehensive input data for in silico prediction of compound activities and potential hazards in drug discovery and toxicology. CP enables the rapid, multiplexed investigation of various molecular mechanisms for thousands of compounds at the single-cell level. The resulting large volumes of image data provide great opportunities but also pose challenges to image and data analysis routines as well as property prediction models. This review addresses the integration of CP-based phenotypic data together with or in substitute of structural information from compounds into machine (ML) and deep learning (DL) models to predict compound activities for various human-relevant disease endpoints and to identify the underlying modes-of-action (MoA) while avoiding unnecessary animal testing. The successful application of CP in combination with powerful ML/DL models promises further advances in understanding compound responses of cells guiding therapeutic development and risk assessment. Therefore, this review highlights the importance of unlocking the potential of CP assays when combined with molecular fingerprints for compound evaluation and discusses the current challenges that are associated with this approach.

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“…Deep learning methodologies, integrated into medicinal chemistry workflows, aim to expedite the DMTA cycle, thereby delivering superior molecules more rapidly. 29–31 While substantial research in machine learning applications has focused on the deployment of generative methods 32–36 and structure-based scoring functions for bioactivity prediction, 37–42 the development of machine learning methods for efficient synthesis planning of complex molecules has emerged as another challenge in the field of drug discovery. 43,44 Especially, graph-based machine learning methods, facilitating efficient learning on three-dimensional (3D) molecular models, have proven instrumental in various domains of chemistry.…”

Section: Introductionmentioning

confidence: 99%