11 papers · ranked by Valyu relevance
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Active learning is an emerging paradigm used to help accelerating drug discovery, but most prior applications seek solely to optimize potency, whereas multiple properties influence a compound’s utility as a drug candidate. We introduce a method for multiobjective ligand optimization, which is able to efficiently handle…
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The increasing importance and predictive power of modern molecular modeling, driven by physics- and machine learning-based methods, necessitates a new collaborative architecture to replace the isolated, traditional model of software development. The traditional approach often led to redundant engineering effort, high…
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Accurate modeling of drug concentration--time (C--t) profiles is central to pharmacokinetics (PK) and plays a critical role in both early-stage compound selection and late-stage individualized dosing. Traditional PK model offer mechanistic interpretability but often rely on rigid assumptions, extensive…
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The political, social and economic consequences of climate change drastically influence the requirements of modern energy systems and its components. This includes not only energy production but also concepts and innovations for its storage, especially in magnitudes of gigawatt hours. Carnot batteries, which convert…
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Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
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Fragment-based drug design (FBDD) has become a key approach in structure-based drug discovery, allowing researchers to systematically develop molecular fragments into potent ligands. Although recent generative AI models, such as diffusion-based approaches, show great potential for designing new molecules, applying them…
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Predicting drug-induced toxicity remains a central challenge in computational toxicology, particularly for organ-specific adverse effects that arise from diverse structural, biochemical, and mechanistic origins. Existing deep learning models excel at pattern recognition but often lack mechanistic interpretability…
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Early prediction of drug-induced organ toxicity remains a major bottleneck in drug discovery and clinical pharmacotherapy. Most data-driven toxicity models behave as endpoint predictors: they output a label but provide limited transparency about why a compound is risky or which evidence channel dominated the decision.…
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Accurate prediction of chemical reaction yields remains essential for accelerating synthesis optimization, yet current machine learning models face critical limitations in capturing temporal dynamics, providing calibrated uncertainty estimates, and explicitly modeling reactant-to-product transformations. Here we…
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Realizing the promise of artificial intelligence (AI) to accelerate scientific progress and deliver technological impact depends on how effectively AI can be integrated into real-world decision- making processes. As Peter Norvig states, “Somewhat remarkably, almost all AI research until very recently has assumed that…
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Molecular dynamics (MD) and hybrid quantum–classical (QM/MM) methods provide powerful tools for simulating chemical and biological systems, yet their application to long-timescale, strongly coupled processes remains limited by non-integrability, chaotic sensitivity, and extreme time-scale separation. These limitations…