Biology subjects

Clark, J. D.

Publications and source records attributed to Clark, J. D..

3 recordsLinked to original sources

bioRxiv2026

Small molecule structure elucidation from NMR spectra with generative and reasoning models

Elucidating the structures of unknown small molecules remains a major challenge in chemistry, pharmacology, and drug discovery. While nuclear magnetic resonance (NMR) spectroscopy is the gold standard for structure elucidation, interpreting spectra remains labor-intensive and requires expert knowledge to navigate the vast space of possible structures. Here, we present an AI system for rapid, automated de novo structure elucidation that combines a molecular generative model with a scientific reasoning agent. Its generative component, ChefNMR2, is an atomic flow-matching model that predicts small-molecule structures from molecular formulae and one-dimensional 1H and 13C NMR spectra. ChefNMR2-Agent augments our generative model with large language models (LLMs) to reason over and refine candidate structures. Across diverse benchmarks, ChefNMR2 substantially outperforms existing tools, offering calibrated confidence estimates and two to three orders of magnitude faster inference than frontier LLMs. We use ChefNMR2-Agent to elucidate the structures of four novel natural products, matching human-chemist accuracy in a fraction of the time. Our work demonstrates how specialized generative models and general-purpose reasoning agents can unite to solve challenging scientific inverse problems and accelerate NMR structure elucidation, thus opening new possibilities for AI-driven small-molecule discovery across diverse disciplines.

bioinformatics↗

bioRxiv2019

Exercise and angiotensin receptor blockade enhance recovery after orthopaedic trauma in mice by decreasing pain and improving muscle regeneration

Chronic pain and disability after limb injury are major public health problems. One key obstacle to addressing these adverse outcomes is that we do not know when exercise should be initiated or whether the beneficial effects of exercise can be reproduced using pharmacological tools. In these studies, we developed and used a murine model of orthopaedic trauma combining tibia fracture and pin fixation with tibialis anterior muscle damage. Behavioral measures included mechanical nociceptive thresholds and distances run on exercise wheels. Bone healing was quantified using microCT scanning, and muscle fiber size distribution as well as fibrosis were followed using immunohistochemistry. We found that the model provided robust mechanical allodynia, fibrosis and a shift to smaller average muscle fiber size lasting up to 5 weeks from injury. We also observed that allowing \"late\" (weeks 1-2) rather than \"early\" (weeks 0-1) wheel running after injury resulted in greater overall running activity and greater reversal of allodynia. In parallel, the late running paradigm was also associated with lower levels of muscle fibrosis and a return towards normal muscle fiber diameters. Providing the anti-fibrotic angiotensin receptor blocker losartan to mice in drinking water blocked TGFbeta production while reducing both allodynia and muscle fibrosis. Combining losartan and late exercise provided no additional benefit. We conclude that early healing after orthopaedic trauma must be allowed prior to the initiation of exercise to achieve optimal pain, functional and physiological outcomes. Losartan may provide many of the same pain, functional and physiological outcomes by its regulation of TGFbeta signaling and is a viable candidate for translational studies.\n\nKey points summaryO_LIOur tibial fracture orthopaedic injury model in mice recapitulates the major manifestations of complex trauma including nociceptive sensitization, bone fracture, muscle fibrosis and muscle fiber loss.\nC_LIO_LIDelayed exercise after complex orthopaedic trauma results in decreased muscle fibrosis and improved pain\nC_LIO_LILosartan, an angiotensin-receptor blocker with antifibrotic abilities, recapitulates the effect of exercise on post-injury recovery\nC_LI

neuroscience↗

bioRxiv2018

Bayesian analysis of isothermal titration calorimetry for binding thermodynamics

Isothermal titration calorimetry (ITC) is the only technique able to determine both the enthalpy and entropy of noncovalent association in a single experiment. The standard data analysis method based on nonlinear regression, however, provides unrealistically small uncertainty estimates due to its neglect of dominant sources of error. Here, we present a Bayesian framework for sampling from the posterior distribution of all thermodynamic parameters and other quantities of interest from one or more ITC experiments, allowing uncertainties and correlations to be quantitatively assessed. For a series of ITC measurements on metal:chelator and protein:ligand systems, the Bayesian approach yields uncertainties which represent the variability from experiment to experiment more accurately than the standard data analysis. In some datasets, the median enthalpy of binding is shifted by as much as 1.5 kcal/mol. A Python implementation suitable for analysis of data generated by MicroCal instruments (and adaptable to other calorimeters) is freely available online.

biophysics↗