Language models can identify enzymatic binding sites in protein sequences.

Recent advances in language modeling have had a tremendous impact on how we handle sequential data in science. Language architectures have emerged as a hotbed of innovation and creativity in natural language processing over the last decade, and have since gained prominence in modeling proteins and chemical processes, elucidating structural relationships from textual/sequential data. Surprisingly, some of these relationships refer to three-dimensional structural features, raising important questions on the dimensionality of the information encoded within sequential data. Here, we demonstrate that the unsupervised use of a language model architecture to a language representation of bio-catalyzed chemical reactions can capture the signal at the base of the substrate-binding site atomic interactions. This allows us to identify the three-dimensional binding site position in unknown protein sequences. The language representation comprises a reaction-simplified molecular-input line-entry system (SMILES) for substrate and products, and amino acid sequence information for the enzyme. This approach can recover, with no supervision, 52.13% of the binding site when considering co-crystallized substrate-enzyme structures as ground truth, vastly outperforming other attention-based models.

Fuelling the Digital Chemistry Revolution with Language Models.

The RXN for Chemistry project, initiated by IBM Research Europe - Zurich in 2017, aimed to develop a series of digital assets using machine learning techniques to promote the use of data-driven methodologies in synthetic organic chemistry. This research adopts an innovative concept by treating chemical reaction data as language records, treating the prediction of a synthetic organic chemistry reaction as a translation task between precursor and product languages. Over the years, the IBM Research team has successfully developed language models for various applications including forward reaction prediction, retrosynthesis, reaction classification, atom-mapping, procedure extraction from text, inference of experimental protocols and its use in programming commercial automation hardware to implement an autonomous chemical laboratory. Furthermore, the project has recently incorporated biochemical data in training models for greener and more sustainable chemical reactions. The remarkable ease of constructing prediction models and continually enhancing them through data augmentation with minimal human intervention has led to the widespread adoption of language model technologies, facilitating the digitalization of chemistry in diverse industrial sectors such as pharmaceuticals and chemical manufacturing. This manuscript provides a concise overview of the scientific components that contributed to the prestigious Sandmeyer Award in 2022.

The Societal and Scientific Importance of Inclusivity, Diversity, and Equity in Machine Learning for Chemistry.

While the introduction of practical deep learning has driven progress across scientific fields, recent research highlighted that the requirement of deep learning for ever-increasing computational resources and data has potential negative impacts on the scientific community and society as a whole. An ever-growing need for more computational resources may exacerbate the concentration of funding, the exclusiveness of research, and thus the inequality between countries, sectors, and institutions. Here, I introduce recent concerns and considerations of the machine learning research community that could affect chemistry and present potential solutions, including more detailed assessments of model performance, increased adherence to open science and open data practices, an increase in multinational and multi-institutional collaboration, and a focus on thematic and cultural diversity.

EnzymeMap: curation, validation and data-driven prediction of enzymatic reactions.

Enzymatic reactions are an ecofriendly, selective, and versatile addition, sometimes even alternative to organic reactions for the synthesis of chemical compounds such as pharmaceuticals or fine chemicals. To identify suitable reactions, computational models to predict the activity of enzymes on non-native substrates, to perform retrosynthetic pathway searches, or to predict the outcomes of reactions including regio- and stereoselectivity are becoming increasingly important. However, current approaches are substantially hindered by the limited amount of available data, especially if balanced and atom mapped reactions are needed and if the models feature machine learning components. We therefore constructed a high-quality dataset (EnzymeMap) by developing a large set of correction and validation algorithms for recorded reactions in the literature and showcase its significant positive impact on machine learning models of retrosynthesis, forward prediction, and regioselectivity prediction, outperforming previous approaches by a large margin. Our dataset allows for deep learning models of enzymatic reactions with unprecedented accuracy, and is freely available online.

An explainability framework for deep learning on chemical reactions exemplified by enzyme-catalysed reaction classification.

Assigning or proposing a catalysing enzyme given a chemical or biochemical reaction is of great interest to life sciences and chemistry alike. The exploration and design of metabolic pathways and the challenge of finding more sustainable enzyme-catalysed alternatives to traditional organic reactions are just two examples of tasks that require an association between reaction and enzyme. However, given the lack of large and balanced annotated data sets of enzyme-catalysed reactions, assigning an enzyme to a reaction still relies on expert-curated rules and databases. Here, we present a data-driven explainable human-in-the-loop machine learning approach to support and ultimately automate the association of a catalysing enzyme with a given biochemical reaction. In addition, the proposed method is capable of predicting enzymes as candidate catalysts for organic reactions amendable to biocatalysis. Finally, the introduced explainability and visualisation methods can easily be generalised to support other machine-learning approaches involving chemical and biochemical reactions.

Alchemical analysis of FDA approved drugs.

Chemical space maps help visualize similarities within molecular sets. However, there are many different molecular similarity measures resulting in a confusing number of possible comparisons. To overcome this limitation, we exploit the fact that tools designed for reaction informatics also work for alchemical processes that do not obey Lavoisier's principle, such as the transmutation of lead into gold. We start by using the differential reaction fingerprint (DRFP) to create tree-maps (TMAPs) representing the chemical space of pairs of drugs selected as being similar according to various molecular fingerprints. We then use the Transformer-based RXNMapper model to understand structural relationships between drugs, and its confidence score to distinguish between pairs related by chemically feasible transformations and pairs related by alchemical transmutations. This analysis reveals a diversity of structural similarity relationships that are otherwise difficult to analyze simultaneously. We exemplify this approach by visualizing FDA-approved drugs, EGFR inhibitors, and polymyxin B analogs.

Quantum chemical data generation as fill-in for reliability enhancement of machine-learning reaction and retrosynthesis planning.

Data-driven synthesis planning has seen remarkable successes in recent years by virtue of modern approaches of artificial intelligence that efficiently exploit vast databases with experimental data on chemical reactions. However, this success story is intimately connected to the availability of existing experimental data. It may well occur in retrosynthetic and synthesis design tasks that predictions in individual steps of a reaction cascade are affected by large uncertainties. In such cases, it will, in general, not be easily possible to provide missing data from autonomously conducted experiments on demand. However, first-principles calculations can, in principle, provide missing data to enhance the confidence of an individual prediction or for model retraining. Here, we demonstrate the feasibility of such an ansatz and examine resource requirements for conducting autonomous first-principles calculations on demand.

What is the Rate of Response to Nonoperative Treatment for Hip-Related Pain? A Systematic Review With Meta-analysis.

OBJECTIVE: We aimed to (1) determine the rate of satisfactory response to nonoperative treatment for nonarthritic hip-related pain, and (2) evaluate the specific effect of various elements of physical therapy and nonoperative treatment options aside from physical therapy. DESIGN: Systematic review with meta-analysis. LITERATURE SEARCH: We searched 7 databases and reference lists of eligible studies from their inception to February 2022. STUDY SELECTION CRITERIA: We included randomized controlled trials and prospective cohort studies that compared a nonoperative management protocol to any other treatment for patients with femoroacetabular impingement syndrome, acetabular dysplasia, acetabular labral tear, and/or nonarthritic hip pain not otherwise specified. DATA SYNTHESIS: We used random-effects meta-analyses, as appropriate. Study quality was assessed using an adapted Downs and Black checklist. Certainty of evidence was assessed using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach. RESULTS: Twenty-six studies (1153 patients) were eligible for qualitative synthesis, and 16 were included in the meta-analysis. Moderate certainty evidence suggests that the overall response rate to nonoperative treatment was 54% (95% confidence interval: 32%, 76%). The overall mean improvement after physical therapy treatment was 11.3 points (7.6-14.9) on 100-point patient-reported hip symptom measures (low to moderate certainty) and 22.2 points (4.6-39.9) on 100-point pain severity measures (low certainty). No definitive specific effect was observed regarding therapy duration or approach (ie, flexibility exercise, movement pattern training, and/or mobilization) (very low to low certainty). Very low to low certainty evidence supported viscosupplementation, corticosteroid injection, and a supportive brace. CONCLUSION: Over half of patients with nonarthritic hip-related pain reported satisfactory response to nonoperative treatment. However, the essential elements of comprehensive nonoperative treatment remain unclear. J Orthop Sports Phys Ther 2023;53(5):1-21. Epub 9 March 2023. doi:10.2519/jospt.2023.11666.

Reaction classification and yield prediction using the differential reaction fingerprint DRFP.

Predicting the nature and outcome of reactions using computational methods is a crucial tool to accelerate chemical research. The recent application of deep learning-based learned fingerprints to reaction classification and reaction yield prediction has shown an impressive increase in performance compared to previous methods such as DFT- and structure-based fingerprints. However, learned fingerprints require large training data sets, are inherently biased, and are based on complex deep learning architectures. Here we present the differential reaction fingerprint DRFP. The DRFP algorithm takes a reaction SMILES as an input and creates a binary fingerprint based on the symmetric difference of two sets containing the circular molecular n-grams generated from the molecules listed left and right from the reaction arrow, respectively, without the need for distinguishing between reactants and reagents. We show that DRFP performs better than DFT-based fingerprints in reaction yield prediction and other structure-based fingerprints in reaction classification, reaching the performance of state-of-the-art learned fingerprints in both tasks while being data-independent.

Biocatalysed synthesis planning using data-driven learning.

Enzyme catalysts are an integral part of green chemistry strategies towards a more sustainable and resource-efficient chemical synthesis. However, the use of biocatalysed reactions in retrosynthetic planning clashes with the difficulties in predicting the enzymatic activity on unreported substrates and enzyme-specific stereo- and regioselectivity. As of now, only rule-based systems support retrosynthetic planning using biocatalysis, while initial data-driven approaches are limited to forward predictions. Here, we extend the data-driven forward reaction as well as retrosynthetic pathway prediction models based on the Molecular Transformer architecture to biocatalysis. The enzymatic knowledge is learned from an extensive data set of publicly available biochemical reactions with the aid of a new class token scheme based on the enzyme commission classification number, which captures catalysis patterns among different enzymes belonging to the same hierarchy. The forward reaction prediction model (top-1 accuracy of 49.6%), the retrosynthetic pathway (top-1 single-step round-trip accuracy of 39.6%) and the curated data set are made publicly available to facilitate the adoption of enzymatic catalysis in the design of greener chemistry processes.

Hepatic epithelioid hemangioendothelioma associated with acute disseminated encephalomyelitis by a possible paraneoplastic process.

Epithelioid hemangioendothelioma (EHE) is a low-grade, malignant vascular neoplasm that frequently involves the liver, lungs, bone, and soft tissue. Although not commonly associated with a paraneoplastic syndrome, paraneoplastic syndromes in the setting of EHE have been reported. Acute disseminated encephalomyelitis (ADEM) is an acute, autoimmune, demyelinating disorder of the central nervous system that most commonly occurs after an infection or vaccination. We present the case of a 23 year old female who developed the acute onset of fevers, tremors, right sided hemiplegia, global aphasia, and incontinence of urine and stool. MRI demonstrated findings consistent with a demyelinating disorder and brain biopsy confirmed the diagnosis of ADEM. The patient's work up revealed multiple liver lesions which were biopsy proven EHE. This case report discusses the diagnosis and treatment of two concurrent rare disease processes and the possible association of the processes via a paraneoplastic syndrome.

The name tells the story: Two-pore channels.

TPC2-A1-N and TPC2-A1-P, two novel small molecules, differentially activate two-pore channel 2 (TPC2) and mimic the activation of TPC2 with NAADP and PIP2, resulting in distinct ion channel selectivities. These two different modes of TPC2 activity have physiological, and possibly pathophysiological, implications as they can modulate vesicle trafficking and lysosomal exocytosis.

One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome.

BACKGROUND: Molecular fingerprints are essential cheminformatics tools for virtual screening and mapping chemical space. Among the different types of fingerprints, substructure fingerprints perform best for small molecules such as drugs, while atom-pair fingerprints are preferable for large molecules such as peptides. However, no available fingerprint achieves good performance on both classes of molecules. RESULTS: Here we set out to design a new fingerprint suitable for both small and large molecules by combining substructure and atom-pair concepts. Our quest resulted in a new fingerprint called MinHashed atom-pair fingerprint up to a diameter of four bonds (MAP4). In this fingerprint the circular substructures with radii of r = 1 and r = 2 bonds around each atom in an atom-pair are written as two pairs of SMILES, each pair being combined with the topological distance separating the two central atoms. These so-called atom-pair molecular shingles are hashed, and the resulting set of hashes is MinHashed to form the MAP4 fingerprint. MAP4 significantly outperforms all other fingerprints on an extended benchmark that combines the Riniker and Landrum small molecule benchmark with a peptide benchmark recovering BLAST analogs from either scrambled or point mutation analogs. MAP4 furthermore produces well-organized chemical space tree-maps (TMAPs) for databases as diverse as DrugBank, ChEMBL, SwissProt and the Human Metabolome Database (HMBD), and differentiates between all metabolites in HMBD, over 70% of which are indistinguishable from their nearest neighbor using substructure fingerprints. CONCLUSION: MAP4 is a new molecular fingerprint suitable for drugs, biomolecules, and the metabolome and can be adopted as a universal fingerprint to describe and search chemical space. The source code is available at https://github.com/reymond-group/map4 and interactive MAP4 similarity search tools and TMAPs for various databases are accessible at http://map-search.gdb.tools/ and http://tm.gdb.tools/map4/.

Visualization of very large high-dimensional data sets as minimum spanning trees.

The chemical sciences are producing an unprecedented amount of large, high-dimensional data sets containing chemical structures and associated properties. However, there are currently no algorithms to visualize such data while preserving both global and local features with a sufficient level of detail to allow for human inspection and interpretation. Here, we propose a solution to this problem with a new data visualization method, TMAP, capable of representing data sets of up to millions of data points and arbitrary high dimensionality as a two-dimensional tree (http://tmap.gdb.tools). Visualizations based on TMAP are better suited than t-SNE or UMAP for the exploration and interpretation of large data sets due to their tree-like nature, increased local and global neighborhood and structure preservation, and the transparency of the methods the algorithm is based on. We apply TMAP to the most used chemistry data sets including databases of molecules such as ChEMBL, FDB17, the Natural Products Atlas, DSSTox, as well as to the MoleculeNet benchmark collection of data sets. We also show its broad applicability with further examples from biology, particle physics, and literature.

Exploring Chemical Space with Machine Learning.

Chemical space is a concept to organize molecular diversity by postulating that different molecules occupy different regions of a mathematical space where the position of each molecule is defined by its properties. Our aim is to develop methods to explicitly explore chemical space in the area of drug discovery. Here we review our implementations of machine learning in this project, including our use of deep neural networks to enumerate the GDB13 database from a small sample set, to generate analogs of drugs and natural products after training with fragment-size molecules, and to predict the polypharmacology of molecules after training with known bioactive compounds from ChEMBL. We also discuss visualization methods for big data as means to keep track and learn from machine learning results. Computational tools discussed in this review are freely available at http://gdb.unibe.ch and https://github.com/reymond-group.

Isolated Axillary Nerve Injury in an Elite High School American Football Player: A Case Report.

An elite high school American football athlete sustained a traumatic, isolated, axillary nerve injury. Axillary nerve injuries are uncommon, but serious injuries in American football. With the advent of nerve transfers and grafts, these injuries, if diagnosed in a timely manner, are treatable. This case report discusses the multidisciplinary approach necessary for the diagnosis and treatment of an elite high school American football player who presented with marked deltoid atrophy. The athlete's injury was diagnosed via electrodiagnostic testing and he underwent a medial triceps nerve to axillary nerve transfer. After appropriate postsurgical therapy, the athlete was able to return to American football the subsequent season and continue performing at an elite level. This case report reviews the evaluation and modern treatment for axillary nerve injuries in the athlete, including nerve transfers, nerve grafts, and return to play.

A Narrative Review of Musculoskeletal Impairments Associated With Nonspecific Chronic Pelvic Pain.

The purpose of this narrative review is to present the evidence relating to musculoskeletal impairments found in people with nonspecific chronic pelvic pain (CPP). The musculoskeletal impairments assessed in this review include pelvic floor muscle: performance, resting state, strength, activation, posture and movement patterns. A search was performed systematically using PubMed, Cochrane, CINAHL, Embase, and Web of Science databases from 1998 to 2018 to identify studies reporting the relationship between nonspecific CPP and musculoskeletal impairments of the hip, pelvis, and trunk. The search resulted in 2106 articles that were screened by two authors. Remaining articles were screened by an additional two authors for inclusion in this review. Thirty-one articles remained after initial screening. Full-text publications were reviewed and an additional 25 articles were excluded. Six additional articles were located through review of the reference lists of included articles. The final review included 12 publications. Seven of these studies were cross-sectional cohorts or case-control comparing patients with CPP to asymptomatic controls. The level of evidence for the studies included in this review was low at Levels 4 and 5. We were unable to draw clear conclusions regarding the relationships of musculoskeletal impairments and CPP because validity and use of terms and assessments were inconsistent. Further research is needed with standardized definitions and measurements to better understand the musculoskeletal system as it relates to nonspecific CPP.