13 papers · ranked by Valyu relevance
Feng Feng, Zhenru Chen, Jianyuan Ni, Yuanxun Zhang + 3 more
Drinking water is essential to public health and socioeconomic growth. Therefore, assessing and ensuring drinking water supply is a critical task in modern society. Conventional approaches to analyzing and controlling drinking water quality are labor-intensive and costly with a low throughput. Machine learning (ML) is…
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Machine learning (ML) is gaining momentum in chemistry for the prediction of various molecular properties. However, these models are often trained on relatively scarce, sometimes low-quality data, resulting in what we describe as memorization (rather than learning) and poorly generalizable models. Aiming to revisit the…
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The effectiveness of machine learning (ML) in drug discovery hinges on evaluation and modeling approaches that align with how compounds are tested and compared in real experimental contexts. We observe that experimental data in public repositories like ChEMBL naturally clusters by assay origin, while retaining…
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Predicting molecular properties is a key challenge in drug discovery. Machine learning models, especially those based on transformer architectures, are increasingly used to make these predictions from chemical structures. Inspired by recent progress in natural language processing, many studies have adopted encoder-only…
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Computational blind challenges offer critical, unbiased assessment opportunities to assess and accelerate scientific progress, as demonstrated by a breadth of breakthroughs over the last decade. We report the outcomes and key insights from an open science community blind challenge focused on computational methods in…
Koichi Handa, Saki Yoshimura, Michiharu Kageyama, Takeshi Iijima
AI is expected to help identify excellent candidates in drug discovery. However, we face a lack of data as it is time consuming and expensive to acquire raw data perfectly for many compounds. Hence, we tried to develop a novel QSAR method to predict a parameter more precisely from an incomplete dataset via optimizing…
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Background: Janus Kinase 2 (JAK2) is a key kinase in cellular signal transduction. Its abnormal activation is closely related to various myeloproliferative neoplasms and inflammatory diseases. Developing selective JAK2 inhibitors is an important direction in drug discovery. Accurate prediction of compound inhibitory…
Fuzhan Rahmanian, Robert M. Lee, Dominik Linzner, Kathrin Michel + 4 more
Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths, form factors, and electrochemical testing protocols. Existing models typically translate poorly across different electrode, electrolyte, and additive materials, mostly require a fixed…
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Machine learning holds significant promise for accelerating biomarker discovery in clinical proteomics, yet its real-world impact remains limited by widespread methodological pitfalls and unrealistic expectations. In this perspective, we critically examine the integration of machine learning into clinical proteomics…
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Proteochemometric models (PCM) are used in computational drug discovery to leverage both protein and ligand representations for bioactivity prediction. While machine learning (ML) and deep learning (DL) have come to dominate PCMs, often serving as scoring functions, rigorous evaluation standards have not always been…
Chi Zhang, Dmytro Antypov, Matthew J Rosseinsky, Matthew Stephen Dyer
Machine learning has found wide application in the materials field, particularly in discovering structure-property relationships. However, its potential in predicting synthetic accessibility of materials remains relatively unexplored due to the lack of negative data. In this study, we employ several one-class…
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Ensuring the trustworthiness of machine learning (ML) models in high-stake applications is crucial. One such application is predicting anti-cancer drug sensitivity, where ML models are built with the final goal of integrating them into treatment recommendation systems for personalized medicine. Here, we propose a…
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LC-HRMS is widely used in forensic toxicology for broad-scope screening. When a newly emerging or rarely encountered compound is tentatively identified, toxicologists must decide whether it may be relevant to the case and, if so, quantify it. Acquiring reference material for quantification is costly and time-consuming.…