MassFormer: Tandem Mass Spectrum Prediction with Graph Transformers

Mass spectrometry is a key tool in the study of small molecules, playing an important role in metabolomics, drug discovery, and environmental chemistry. Tandem mass spectra capture fragmentation patterns that provide key structural information about a molecule and help with its identification. Practitioners often rely on spectral library searches to match unknown spectra with known compounds. However, such search-based methods are limited by availability of reference experimental data. In this work we show that graph transformers can be used to accurately predict tandem mass spectra. Our model, MassFormer, outperforms competing deep learning approaches for spectrum prediction, and includes an interpretable attention mechanism to help explain predictions. We demonstrate that our model can be used to improve reference library coverage on a synthetic molecule identification task. Through quantitative analysis and visual inspection, we verify that our model recovers prior knowledge about the effect of collision energy on the generated spectrum. We evaluate our model on different types of mass spectra from two independent MS datasets and show that its performance generalizes. Code available at github.com/Roestlab/massformer.Comment: 14 pages (10 without bibliography), 5 figures, 3 table

Primary and secondary defences of squid to cruising and ambush fish predators : variable tactics and their survival value

Author Posting. © The Author(s), 2010. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Animal Behaviour 81 (2011): 585-594, doi:10.1016/j.anbehav.2010.12.002.Longfin squid (Loligo pealeii) were exposed to two predators, bluefish (Pomatomus saltatrix) and summer flounder (Paralichthys dentatus), representing cruising and ambush foraging tactics, respectively. During 35 trials, 86 predator–prey interactions were evaluated between bluefish and squid, and in 29 trials, 92 interactions were assessed between flounder and squid. With bluefish, squid predominantly used stay tactics (68.6%, 59/86) as initial responses. The most common stay response was to drop to the bottom, while showing a disruptive body pattern, and remain motionless. In 37.0% (34/92) of interactions with flounder, squid did not detect predators camouflaging on the bottom and showed no reaction prior to being attacked. Squid that did react, used flee tactics more often as initial responses (43.5%, 40/92), including flight with or without inking. When all defence behaviours were considered concurrently, flight was identified as the strongest predictor of squid survival during interactions with each predator. Squid that used flight at any time during an attack sequence had high probabilities of survival with bluefish (65%, 20/31) and flounder (51%, 18/35). The most important deimatic/protean behaviour used by squid was inking. Inking caused bluefish to startle (deimatic) and abandon attacks (probability of survival = 61%, 11/18) and caused flounder to misdirect (protean) attacks towards ink plumes rather than towards squid (probability of survival = 56%, 14/25). These are the first published laboratory experiments to evaluate the survival value of antipredator behaviours in a cephalopod. Results demonstrate that squid vary their defence tactics in response to different predators and that the effectiveness of antipredator behaviours is contingent upon the behavioural characteristics of the predator encountered.This study was funded by the Woods Hole Oceanographic Institution Sea Grant Program, the Massachusetts Marine Fisheries Institute, the University of Massachusetts and the Five College Coastal and Marine Sciences Program. R. T. Hanlon acknowledges partial support from ONR grant N000140610202 and the Sholley Foundation

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, SMILES, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, SMILES has several shortcomings -- most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100\% robustness: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete Future Projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.Comment: 34 pages, 15 figures, comments and suggestions for additional references are welcome

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, SMILES, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, SMILES has several shortcomings -- most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100\% robustness: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete Future Projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, Smiles, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, Smiles has several shortcomings—most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete future projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science

Space Science Opportunities Augmented by Exploration Telepresence

Since the end of the Apollo missions to the lunar surface in December 1972, humanity has exclusively conducted scientific studies on distant planetary surfaces using teleprogrammed robots. Operations and science return for all of these missions are constrained by two issues related to the great distances between terrestrial scientists and their exploration targets: high communication latencies and limited data bandwidth. Despite the proven successes of in-situ science being conducted using teleprogrammed robotic assets such as Spirit, Opportunity, and Curiosity rovers on the surface of Mars, future planetary field research may substantially overcome latency and bandwidth constraints by employing a variety of alternative strategies that could involve: 1) placing scientists/astronauts directly on planetary surfaces, as was done in the Apollo era; 2) developing fully autonomous robotic systems capable of conducting in-situ field science research; or 3) teleoperation of robotic assets by humans sufficiently proximal to the exploration targets to drastically reduce latencies and significantly increase bandwidth, thereby achieving effective human telepresence. This third strategy has been the focus of experts in telerobotics, telepresence, planetary science, and human spaceflight during two workshops held from October 3–7, 2016, and July 7–13, 2017, at the Keck Institute for Space Studies (KISS). Based on findings from these workshops, this document describes the conceptual and practical foundations of low-latency telepresence (LLT), opportunities for using derivative approaches for scientific exploration of planetary surfaces, and circumstances under which employing telepresence would be especially productive for planetary science. An important finding of these workshops is the conclusion that there has been limited study of the advantages of planetary science via LLT. A major recommendation from these workshops is that space agencies such as NASA should substantially increase science return with greater investments in this promising strategy for human conduct at distant exploration sites

Space Science Opportunities Augmented by Exploration Telepresence

Since the end of the Apollo missions to the lunar surface in December 1972, humanity has exclusively conducted scientific studies on distant planetary surfaces using teleprogrammed robots. Operations and science return for all of these missions are constrained by two issues related to the great distances between terrestrial scientists and their exploration targets: high communication latencies and limited data bandwidth. Despite the proven successes of in-situ science being conducted using teleprogrammed robotic assets such as Spirit, Opportunity, and Curiosity rovers on the surface of Mars, future planetary field research may substantially overcome latency and bandwidth constraints by employing a variety of alternative strategies that could involve: 1) placing scientists/astronauts directly on planetary surfaces, as was done in the Apollo era; 2) developing fully autonomous robotic systems capable of conducting in-situ field science research; or 3) teleoperation of robotic assets by humans sufficiently proximal to the exploration targets to drastically reduce latencies and significantly increase bandwidth, thereby achieving effective human telepresence. This third strategy has been the focus of experts in telerobotics, telepresence, planetary science, and human spaceflight during two workshops held from October 3–7, 2016, and July 7–13, 2017, at the Keck Institute for Space Studies (KISS). Based on findings from these workshops, this document describes the conceptual and practical foundations of low-latency telepresence (LLT), opportunities for using derivative approaches for scientific exploration of planetary surfaces, and circumstances under which employing telepresence would be especially productive for planetary science. An important finding of these workshops is the conclusion that there has been limited study of the advantages of planetary science via LLT. A major recommendation from these workshops is that space agencies such as NASA should substantially increase science return with greater investments in this promising strategy for human conduct at distant exploration sites

Prognostic Factors for Recovery of Vision in Canine Optic Neuritis of Unknown Etiology: 26 Dogs (2003–2018)

Optic neuritis (ON) is a recognized condition, yet factors influencing recovery of vision are currently unknown. The purpose of this study was to identify prognostic factors for recovery of vision in canine ON of unknown etiology. Clinical databases of three referral hospitals were searched for dogs with presumptive ON based on clinicopathologic, MRI/CT, and fundoscopic findings. Twenty-six dogs diagnosed with presumptive ON of unknown etiology, isolated (I-ON) and MUE-associated (MUE-ON), were included in the study. Their medical records were reviewed retrospectively, and the association of complete recovery of vision with signalment, clinicopathologic findings, and treatment was investigated. Datasets were tested for normality using the D'Agostino and Shapiro-Wilk tests. Individual datasets were compared using the Chi-squared test, Fisher's exact test, and the Mann-Whitney U-test. For multiple comparisons with parametric datasets, the one-way analysis of variance (ANOVA) was performed, and for non-parametric datasets, the Kruskal-Wallis test was performed to test for independence. For all data, averages are expressed as median with interquartile range and significance set at p < 0.05. Twenty-six dogs met the inclusion criteria. Median follow-up was 230 days (range 21–1901 days, mean 496 days). Six dogs (23%) achieved complete recovery and 20 dogs (77%) incomplete or no recovery of vision. The presence of a reactive pupillary light reflex (p = 0.013), the absence of fundoscopic lesions (p = 0.0006), a younger age (p = 0.038), and a lower cerebrospinal fluid (CSF) total nucleated cell count (TNCC) (p = 0.022) were statistically associated with complete recovery of vision. Dogs with I-ON were significantly younger (p = 0.046) and had lower CSF TNCC (p = 0.030) compared to the MUE-ON group. This study identified prognostic factors that may influence complete recovery of vision in dogs with ON. A larger cohort of dogs is required to determine whether these findings are robust and whether additional parameters aid accurate prognosis for recovery of vision in canine ON

Photochemical dihydrogen production using an analogue of the active site of [NiFe] hydrogenase

The photoproduction of dihydrogen (H2) by a low molecular weight analogue of the active site of [NiFe] hydrogenase has been investigated by the reduction of the [NiFe2] cluster, 1, by a photosensitier PS (PS = [ReCl(CO)3(bpy)] or [Ru(bpy)3][PF6]2). Reductive quenching of the 3MLCT excited state of the photosensitiser by NEt3 or N(CH2CH2OH)3 (TEOA) generates PS•−, and subsequent intermolecular electron transfer to 1 produces the reduced anionic form of 1. Time-resolved infrared spectroscopy (TRIR) has been used to probe the intermediates throughout the reduction of 1 and subsequent photocatalytic H2 production from [HTEOA][BF4], which was monitored by gas chromatography. Two structural isomers of the reduced form of 1 (1a•− and 1b•−) were detected by Fourier transform infrared spectroscopy (FTIR) in both CH3CN and DMF (dimethylformamide), while only 1a•− was detected in CH2Cl2. Structures for these intermediates are proposed from the results of density functional theory calculations and FTIR spectroscopy. 1a•− is assigned to a similar structure to 1 with six terminal carbonyl ligands, while calculations suggest that in 1b•− two of the carbonyl groups bridge the Fe centres, consistent with the peak observed at 1714 cm−1 in the FTIR spectrum for 1b•− in CH3CN, assigned to a ν(CO) stretching vibration. The formation of 1a•− and 1b•− and the production of H2 was studied in CH3CN, DMF and CH2Cl2. Although the more catalytically active species (1a•− or 1b•−) could not be determined, photocatalysis was observed only in CH3CN and DMF