Transformers and Large Language Models for Chemistry and Drug Discovery

Language modeling has seen impressive progress over the last years, mainly prompted by the invention of the Transformer architecture, sparking a revolution in many fields of machine learning, with breakthroughs in chemistry and biology. In this chapter, we explore how analogies between chemical and natural language have inspired the use of Transformers to tackle important bottlenecks in the drug discovery process, such as retrosynthetic planning and chemical space exploration. The revolution started with models able to perform particular tasks with a single type of data, like linearised molecular graphs, which then evolved to include other types of data, like spectra from analytical instruments, synthesis actions, and human language. A new trend leverages recent developments in large language models, giving rise to a wave of models capable of solving generic tasks in chemistry, all facilitated by the flexibility of natural language. As we continue to explore and harness these capabilities, we can look forward to a future where machine learning plays an even more integral role in accelerating scientific discovery

14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon

Chemistry and materials science are complex. Recently, there have been great successes in addressing this complexity using data-driven or computational techniques. Yet, the necessity of input structured in very specific forms and the fact that there is an ever-growing number of tools creates usability and accessibility challenges. Coupled with the reality that much data in these disciplines is unstructured, the effectiveness of these tools is limited. Motivated by recent works that indicated that large language models (LLMs) might help address some of these issues, we organized a hackathon event on the applications of LLMs in chemistry, materials science, and beyond. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines

The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20\%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors

Global overview of the management of acute cholecystitis during the COVID-19 pandemic (CHOLECOVID study)

Background: This study provides a global overview of the management of patients with acute cholecystitis during the initial phase of the COVID-19 pandemic. Methods: CHOLECOVID is an international, multicentre, observational comparative study of patients admitted to hospital with acute cholecystitis during the COVID-19 pandemic. Data on management were collected for a 2-month study interval coincident with the WHO declaration of the SARS-CoV-2 pandemic and compared with an equivalent pre-pandemic time interval. Mediation analysis examined the influence of SARS-COV-2 infection on 30-day mortality. Results: This study collected data on 9783 patients with acute cholecystitis admitted to 247 hospitals across the world. The pandemic was associated with reduced availability of surgical workforce and operating facilities globally, a significant shift to worse severity of disease, and increased use of conservative management. There was a reduction (both absolute and proportionate) in the number of patients undergoing cholecystectomy from 3095 patients (56.2 per cent) pre-pandemic to 1998 patients (46.2 per cent) during the pandemic but there was no difference in 30-day all-cause mortality after cholecystectomy comparing the pre-pandemic interval with the pandemic (13 patients (0.4 per cent) pre-pandemic to 13 patients (0.6 per cent) pandemic; P = 0.355). In mediation analysis, an admission with acute cholecystitis during the pandemic was associated with a non-significant increased risk of death (OR 1.29, 95 per cent c.i. 0.93 to 1.79, P = 0.121). Conclusion: CHOLECOVID provides a unique overview of the treatment of patients with cholecystitis across the globe during the first months of the SARS-CoV-2 pandemic. The study highlights the need for system resilience in retention of elective surgical activity. Cholecystectomy was associated with a low risk of mortality and deferral of treatment results in an increase in avoidable morbidity that represents the non-COVID cost of this pandemic

Periodic system converges and is affected by wars

The periodic system emerges by intertwining order and similarity relationships among chemical elements, which in turn arise from known substances at a given time that constitute the chemical space. Although the system has been adjusted to accommodate new elements, the connection with the chemical space has been largely forgotten and the question that arises is about the effect of the exponentially growing chemical space upon the periodic system. To what extent advances in chemistry have confirmed or distorted the periodic system? Is the system --icon of chemistry-- a traversal feature of the chemical space? Here we solve these questions by computationally analysing the effect of the chemical space upon the periodic system from the dawn of the 19th century until the present. We found that although the system has undergone several and significant changes across history, it converges towards a stable structure. This dynamics results from advances in chemistry such as the discovery of elements, of forms of chemical combination and the incorporation of new theoretical frameworks. Interestingly, the periodic system is also influenced by socio-political events such as wars. Given the nature of the chemical space, which holds the inertia of more than 200 years of chemical practice, and the limited chemical possibilities for the remaining elements to be synthesised, we hypothesise that the periodic system is going to remain largely untouched in the years to come. We expect our results and methods trigger further research and discussion in the history, pedagogy, philosophy, and ultimately, in the practice of chemistry

Primary cutaneous cryptococcal infection due to fingolimod – Induced lymphopenia with literature review

Cryptococcus. Neoformans (C. neoformans) is an encapsulated heterobasidiomycetous fungus responsible for opportunistic infections worldwide in immunocompromised patients. Clinical presentation ranges from asymptomatic respiratory tract colonization to disseminated infection in any human body part. The central nervous system (CNS) and pulmonary diseases garner most of the clinical attention. Secondary cutaneous cryptococcosis is an uncommon manifestation seen as a sentinel sign commonly in disseminated cryptococcal infection. Primary cutaneous cryptococcosis (PCC) is a rare manifestation seen in both immunocompromised and immunocompetent patients. It is a discrete infection with different epidemiological trends. Immunosuppressive therapy (corticosteroids, tacrolimus) predisposes a patient to acquire this clinical entity. We present a case of an elderly Caucasian male on fingolimod for relapsing-remitting multiple sclerosis with nonhealing scalp lesions for four years. He was a referral to our healthcare center for the presence of fungal elements seen on a scalp biopsy fungal stains. Final cultures returned positive for C. neoformans susceptible to fluconazole (MIC = 8 μg/mL). The CD4 count was 13 cells/uL, and workup for CNS and disseminated cryptococcal infection were negative. Fingolimod is an immunomodulator that acts on sphingosine 1-phosphate receptors, affecting the lymphocytes. Pubmed literature review revealed few case reports (< 5) with PCC in patients on fingolimod. To our knowledge, ours is the first case with scalp cryptococcosis, with the lowest CD4 count while being on fingolimod. No randomized controlled trial data exist for the treatment of PCC. Therapy initiated with oral luconazole for six months with significant improvement at three months

Cytomegalovirus pneumonitis-induced secondary hemophagocytic lymphohistiocytosis and SIADH in an immunocompetent elderly male literature review

Hemophagocytic lymphohistiocytosis (HLH) is also known as hemophagocytic syndrome. It is a lethal hematologic condition due to a dysregulated immune response which results in inappropriately activated macrophages damaging host tissues. Based on the etiology, HLH can be primary (genetic) or secondary (acquired). The most common cause of a secondary HLH is an infection. Viral infections are the most common cause of secondary HLH. Among the viral causes of secondary HLH, Epstein–Barr virus is the most common etiologic agent. Cytomegalovirus (CMV) is a common causative pathogen in the immunocompromised host but is rare in an immunocompetent adult. In infection- associated secondary HLH, treatment includes antimicrobial therapy. HLH carries a high mortality and morbidity rate as it is an underdiagnosed clinical condition. Successful early diagnosis allows for adequate time for curative therapy. Treatment for HLH includes chemotherapy, immunomodulators, and a hematopoietic stem-cell transplant. The 2004 diagnostic criteria set by the Histiocyte Society serves as a guide to make an earlier clinical diagnosis. A review of PubMed literature revealed only five reported cases of CMV-induced HLH. We describe the sixth case of CMV pneumonitis-induced HLH and syndrome of inappropriate antidiuretic hormone secretion in a 72-year-old White male. He was treated successfully with oral valganciclovir and corticosteroids

14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon

Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines

14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon †

Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines

Global overview of the management of acute cholecystitis during the COVID-19 pandemic (CHOLECOVID study)

Background: This study provides a global overview of the management of patients with acute cholecystitis during the initial phase of the COVID-19 pandemic. Methods: CHOLECOVID is an international, multicentre, observational comparative study of patients admitted to hospital with acute cholecystitis during the COVID-19 pandemic. Data on management were collected for a 2-month study interval coincident with the WHO declaration of the SARS-CoV-2 pandemic and compared with an equivalent pre-pandemic time interval. Mediation analysis examined the influence of SARS-COV-2 infection on 30-day mortality. Results: This study collected data on 9783 patients with acute cholecystitis admitted to 247 hospitals across the world. The pandemic was associated with reduced availability of surgical workforce and operating facilities globally, a significant shift to worse severity of disease, and increased use of conservative management. There was a reduction (both absolute and proportionate) in the number of patients undergoing cholecystectomy from 3095 patients (56.2 per cent) pre-pandemic to 1998 patients (46.2 per cent) during the pandemic but there was no difference in 30-day all-cause mortality after cholecystectomy comparing the pre-pandemic interval with the pandemic (13 patients (0.4 per cent) pre-pandemic to 13 patients (0.6 per cent) pandemic; P = 0.355). In mediation analysis, an admission with acute cholecystitis during the pandemic was associated with a non-significant increased risk of death (OR 1.29, 95 per cent c.i. 0.93 to 1.79, P = 0.121). Conclusion: CHOLECOVID provides a unique overview of the treatment of patients with cholecystitis across the globe during the first months of the SARS-CoV-2 pandemic. The study highlights the need for system resilience in retention of elective surgical activity. Cholecystectomy was associated with a low risk of mortality and deferral of treatment results in an increase in avoidable morbidity that represents the non-COVID cost of this pandemic