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Book ; Online: Isotope ratios and trace element concentrations of basalts from DSDP Leg 15 sites (Appendix 1), supplementary data to: Thompson, Patricia M E; Kempton, Pamela D; White, Rosalind V; Kerr, Andrew C; Tarney, J; Saunders, Andrew D; Fitton, J Godfrey; McBirney, A (2003): Hf-Nd isotope constraints on the origin of the Cretaceous Caribbean plateau and its relationship to the Galápagos plume. Earth and Planetary Science Letters, 217(1-2), 59-75

Thompson, Patricia M E / Fitton, J Godfrey / Kempton, Pamela D / Kerr, Andrew C / McBirney, A / Saunders, Andrew D / Tarney, J / White, Rosalind V

2003

Abstract: Formation of the Cretaceous Caribbean plateau, including the komatiites of Gorgona, has been linked to the currently active Galápagos hotspot. We use Hf-Nd isotopes and trace element data to characterise both the Caribbean plateau and the Galápagos ... ...

Abstract	Formation of the Cretaceous Caribbean plateau, including the komatiites of Gorgona, has been linked to the currently active Galápagos hotspot. We use Hf-Nd isotopes and trace element data to characterise both the Caribbean plateau and the Galápagos hotspot, and to investigate the relationship between them. Four geochemical components are identified in the Galápagos mantle plume: two 'enriched' components with epsilon-Hf and epsilon-Nd similar to enriched components observed in other mantle plumes, one moderately enriched component with high Nb/Y, and a fourth component which most likely represents depleted MORB source mantle. The Caribbean plateau basalt data form a linear array in Hf-Nd isotope space, consistent with mixing between two mantle components. Combined Hf-Nd-Pb-Sr-He isotope and trace element data from this study and the literature suggest that the more enriched Caribbean end member corresponds to one or both of the enriched components identified on Galápagos. Likewise, the depleted end member of the array is geochemically indistinguishable from MORB and corresponds to the depleted component of the Galápagos system. Enriched basalts from Gorgona partially overlap with the Caribbean plateau array in epsilon-Hf vs. epsilon-Nd, whereas depleted basalts, picrites and komatiites from Gorgona have a high epsilon-Hf for a given epsilon-Nd, defining a high-epsilon-Hf depleted end member that is not observed elsewhere within the Caribbean plateau sequences. This component is similar, however, in terms of Hf-Nd-Pb-He isotopes and trace elements to the depleted plume component recognised in basalts from Iceland and along the Reykjanes Ridge. We suggest that the Caribbean plateau represents the initial outpourings of the ancestral Galápagos plume. Absence of a moderately enriched, high Nb/Y component in the older Caribbean plateau (but found today on the island of Floreana) is either due to changing source compositions of the plume over its 90 Ma history, or is an artifact of limited sampling. The high-epsilon-Hf depleted component sampled by the Gorgona komatiites and depleted basalts is unique to Gorgona and is not found in the Caribbean plateau. This may be an indication of the scale of heterogeneity of the Caribbean plateau system; alternatively Gorgona may represent a separate oceanic plateau derived from a completely different Pacific plume, such as the Sala y Gomez.
Language	English
Dates of publication	2003-9999
Size	Online-Ressource
Publisher	PANGAEA - Data Publisher for Earth & Environmental Science
Publishing place	Bremen/Bremerhaven
Document type	Book ; Online
Note	This dataset is supplement to doi:10.1016/S0012-821X(03)00542-9
DOI	10.1594/PANGAEA.725469
Database	Library catalogue of the German National Library of Science and Technology (TIB), Hannover

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Article ; Online: The future of chemistry is language.

White, Andrew D

Nature reviews. Chemistry

2023 Volume 7, Issue 7, Page(s) 457–458

MeSH term(s)	Language ; Chemical Phenomena
Language	English
Publishing date	2023-05-19
Publishing country	England
Document type	Journal Article
ISSN	2397-3358
ISSN (online)	2397-3358
DOI	10.1038/s41570-023-00502-0
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Predicting small molecules solubility on endpoint devices using deep ensemble neural networks.

Ramos, Mayk Caldas / White, Andrew D

Digital discovery

2024 Volume 3, Issue 4, Page(s) 786–795

Abstract: Aqueous solubility is a valuable yet challenging property to predict. Computing solubility using first-principles methods requires accounting for the competing effects of entropy and enthalpy, resulting in long computations for relatively poor accuracy. ... ...

Abstract	Aqueous solubility is a valuable yet challenging property to predict. Computing solubility using first-principles methods requires accounting for the competing effects of entropy and enthalpy, resulting in long computations for relatively poor accuracy. Data-driven approaches, such as deep learning, offer improved accuracy and computational efficiency but typically lack uncertainty quantification. Additionally, ease of use remains a concern for any computational technique, resulting in the sustained popularity of group-based contribution methods. In this work, we addressed these problems with a deep learning model with predictive uncertainty that runs on a static website (without a server). This approach moves computing needs onto the website visitor without requiring installation, removing the need to pay for and maintain servers. Our model achieves satisfactory results in solubility prediction. Furthermore, we demonstrate how to create molecular property prediction models that balance uncertainty and ease of use. The code is available at https://github.com/ur-whitelab/mol.dev, and the model is useable at https://mol.dev.
Language	English
Publishing date	2024-03-13
Publishing country	England
Document type	Journal Article
ISSN	2635-098X
ISSN (online)	2635-098X
DOI	10.1039/d3dd00217a
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Deep Learning for Molecules and Materials.

White, Andrew D

Living journal of computational molecular science

2022 Volume 3, Issue 1

Abstract: Deep learning is becoming a standard tool in chemistry and materials science. Although there are learning materials available for deep learning, none cover the applications in chemistry and materials science or the peculiarities of working with molecules. ...

Abstract	Deep learning is becoming a standard tool in chemistry and materials science. Although there are learning materials available for deep learning, none cover the applications in chemistry and materials science or the peculiarities of working with molecules. The textbook described here provides a systematic and applied introduction to the latest research in deep learning in chemistry and materials science. It covers the math fundamentals, the requisite machine learning, the common neural network architectures used today, and the details necessary to be a practitioner of deep learning. The textbook is a living document and will be updated as the rapidly changing deep learning field evolves.
Language	English
Publishing date	2022-07-05
Publishing country	United States
Document type	Journal Article
ISSN	2575-6524
ISSN (online)	2575-6524
DOI	10.33011/livecoms.3.1.1499
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Bloom filters for molecules.

Medina, Jorge / White, Andrew D

Journal of cheminformatics

2023 Volume 15, Issue 1, Page(s) 95

Abstract: Ultra-large chemical libraries are reaching 10s to 100s of billions of molecules. A challenge for these libraries is to efficiently check if a proposed molecule is present. Here we propose and study Bloom filters for testing if a molecule is present in a ...

Abstract	Ultra-large chemical libraries are reaching 10s to 100s of billions of molecules. A challenge for these libraries is to efficiently check if a proposed molecule is present. Here we propose and study Bloom filters for testing if a molecule is present in a set using either string or fingerprint representations. Bloom filters are small enough to hold billions of molecules in just a few GB of memory and check membership in sub milliseconds. We found string representations can have a false positive rate below 1% and require significantly less storage than using fingerprints. Canonical SMILES with Bloom filters with the simple FNV (Fowler-Noll-Voll) hashing function provide fast and accurate membership tests with small memory requirements. We provide a general implementation and specific filters for detecting if a molecule is purchasable, patented, or a natural product according to existing databases at https://github.com/whitead/molbloom .
Language	English
Publishing date	2023-10-12
Publishing country	England
Document type	Journal Article
ZDB-ID	2486539-4
ISSN	1758-2946
ISSN	1758-2946
DOI	10.1186/s13321-023-00765-1
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Learning Peptide Properties with Positive Examples Only.

Ansari, Mehrad / White, Andrew D

bioRxiv : the preprint server for biology

2023

Abstract: Deep learning can create accurate predictive models by exploiting existing large-scale experimental data, and guide the design of molecules. However, a major barrier is the requirement of both positive and negative examples in the classical supervised ... ...

Abstract	Deep learning can create accurate predictive models by exploiting existing large-scale experimental data, and guide the design of molecules. However, a major barrier is the requirement of both positive and negative examples in the classical supervised learning frameworks. Notably, most peptide databases come with missing information and low number of observations on negative examples, as such sequences are hard to obtain using high-throughput screening methods. To address this challenge, we solely exploit the limited known positive examples in a semi-supervised setting, and discover peptide sequences that are likely to map to certain antimicrobial properties via positive-unlabeled learning (PU). In particular, we use the two learning strategies of adapting base classifier and reliable negative identification to build deep learning models for inferring solubility, hemolysis, binding against SHP-2, and non-fouling activity of peptides, given their sequence. We evaluate the predictive performance of our PU learning method and show that by only using the positive data, it can achieve competitive performance when compared with the classical positive-negative (PN) classification approach, where there is access to both positive and negative examples.
Language	English
Publishing date	2023-06-05
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.06.01.543289
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Serverless Prediction of Peptide Properties with Recurrent Neural Networks.

Ansari, Mehrad / White, Andrew D

Journal of chemical information and modeling

2023 Volume 63, Issue 8, Page(s) 2546–2553

Abstract: We present three deep learning sequence-based prediction models for peptide properties including hemolysis, solubility, and resistance to nonspecific interactions that achieve comparable results to the state-of-the-art models. Our sequence-based ... ...

Abstract	We present three deep learning sequence-based prediction models for peptide properties including hemolysis, solubility, and resistance to nonspecific interactions that achieve comparable results to the state-of-the-art models. Our sequence-based solubility predictor, MahLooL, outperforms the current state-of-the-art methods for short peptides. These models are implemented as a static website without the use of a dedicated server or cloud computing. Web-based models like this allow for accessible and effective reproducibility. Most existing approaches rely on third-party servers that typically require upkeep and maintenance. Our predictive models do not require servers, require no installation of dependencies, and work across a range of devices. The specific architecture is bidirectional recurrent neural networks. This
MeSH term(s)	Reproducibility of Results ; Neural Networks, Computer ; Peptides ; Machine Learning ; Cloud Computing
Chemical Substances	Peptides
Language	English
Publishing date	2023-04-03
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	190019-5
ISSN	1549-960X ; 0095-2338
ISSN (online)	1549-960X
ISSN	0095-2338
DOI	10.1021/acs.jcim.2c01317
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Article ; Online: Inferences of Masculinity and Femininity Across Intersections of Social Class and Gender: A Social Structural Perspective.

White, Andrew D / Diekman, Amanda B

Personality & social psychology bulletin

2023 , Page(s) 1461672231204487

Abstract: This research employs a social structural perspective to analyze the content of intersectional social class and gender stereotypes. We investigated how the structural positioning of class and gender categories differentially foster inferences of ... ...

Abstract	This research employs a social structural perspective to analyze the content of intersectional social class and gender stereotypes. We investigated how the structural positioning of class and gender categories differentially foster inferences of masculinity and femininity. The social structures that organize class and gender differ: Class is marked by access to resources, and gender is marked by a division of labor for care work. Thus, we examined whether masculinity inferences more strongly varied by social class and whether femininity inferences more strongly varied by gender categories. In Study 1, a total 427 undergraduates provided open-ended descriptions of social class and gender groups. In Study 2, a total 758 undergraduates rated the same groups on preselected trait measures. In Study 3, a total 83 adult participants considered a vignette that manipulated a target's structural resources and gender. Across datasets, variation in social class primarily influenced inferences about masculinity while variation in gender primarily influenced inferences about femininity.
Language	English
Publishing date	2023-11-06
Publishing country	United States
Document type	Journal Article
ZDB-ID	2047603-6
ISSN	1552-7433 ; 0146-1672
ISSN (online)	1552-7433
ISSN	0146-1672
DOI	10.1177/01461672231204487
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Bloom filters for molecules

Jorge Medina / Andrew D. White

Journal of Cheminformatics, Vol 15, Iss 1, Pp 1-

2023 Volume 6

Abstract: Abstract Ultra-large chemical libraries are reaching 10s to 100s of billions of molecules. A challenge for these libraries is to efficiently check if a proposed molecule is present. Here we propose and study Bloom filters for testing if a molecule is ... ...

Abstract	Abstract Ultra-large chemical libraries are reaching 10s to 100s of billions of molecules. A challenge for these libraries is to efficiently check if a proposed molecule is present. Here we propose and study Bloom filters for testing if a molecule is present in a set using either string or fingerprint representations. Bloom filters are small enough to hold billions of molecules in just a few GB of memory and check membership in sub milliseconds. We found string representations can have a false positive rate below 1% and require significantly less storage than using fingerprints. Canonical SMILES with Bloom filters with the simple FNV (Fowler-Noll-Voll) hashing function provide fast and accurate membership tests with small memory requirements. We provide a general implementation and specific filters for detecting if a molecule is purchasable, patented, or a natural product according to existing databases at https://github.com/whitead/molbloom .
Keywords	Bloom filter ; Fingerprint ; SMILES ; Hashing ; Information technology ; T58.5-58.64 ; Chemistry ; QD1-999
Subject code	541
Language	English
Publishing date	2023-10-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Symmetric Molecular Dynamics.

Cox, Sam / White, Andrew D

Journal of chemical theory and computation

2022 Volume 18, Issue 7, Page(s) 4077–4081

Abstract: We derive a formulation of molecular dynamics that generates only symmetric configurations. We implement it for all 2D planar and 3D space groups. An atlas of 2D Lennard-Jones crystals under all planar groups is created with symmetric molecular dynamics. ...

Abstract	We derive a formulation of molecular dynamics that generates only symmetric configurations. We implement it for all 2D planar and 3D space groups. An atlas of 2D Lennard-Jones crystals under all planar groups is created with symmetric molecular dynamics.
MeSH term(s)	Molecular Dynamics Simulation
Language	English
Publishing date	2022-06-14
Publishing country	United States
Document type	Journal Article
ISSN	1549-9626
ISSN (online)	1549-9626
DOI	10.1021/acs.jctc.2c00401
Database	MEDical Literature Analysis and Retrieval System OnLINE

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