The blood-brain barrier (BBB) serves as a protective barrier that separates the brain from the circulatory system, regulating the passage of substances into the central nervous system. Assessing the BBB permeability of potential drugs is crucial for effective drug targeting. However, traditional experimental methods for measuring BBB permeability are challenging and impractical for large-scale screening. Consequently, there is a need to develop computational approaches to predict BBB permeability. This paper proposes a GPS Transformer architecture augmented with Self Attention, designed to perform well in the low-data regime. The proposed approach achieved a state-of-the-art performance on the BBB permeability prediction task using the BBBP dataset, surpassing existing models. With a ROC-AUC of 78.8%, the approach sets a state-of-the-art by 5.5%. We demonstrate that standard Self Attention coupled with GPS transformer performs better than other variants of attention coupled with GPS Transformer.

While various models and computational tools have been proposed for structure and property analysis of molecules, generating molecules that conform to all desired structures and properties remains a challenge. Here, we introduce a multi-constraint molecular generation large language model, TSMMG, which, akin to a student, incorporates knowledge from various small models and tools, namely, the 'teachers'. To train TSMMG, we construct a large set of text-molecule pairs by extracting molecular knowledge from these 'teachers', enabling it to generate novel molecules that conform to the descriptions through various text prompts. We experimentally show that TSMMG remarkably performs in generating molecules meeting complex, natural language-described property requirements across two-, three-, and four-constraint tasks, with an average molecular validity of over 99% and success ratio of 82.58%, 68.03%, and 67.48%, respectively. The model also exhibits adaptability through zero-shot testing, creating molecules that satisfy combinations of properties that have not been encountered. It can comprehend text inputs with various language styles, extending beyond the confines of outlined prompts, as confirmed through empirical validation. Additionally, the knowledge distillation feature of TSMMG contributes to the continuous enhancement of small models, while the innovative approach to dataset construction effectively addresses the issues of data scarcity and quality, which positions TSMMG as a promising tool in the domains of drug discovery and materials science.

The introduction of computational techniques to analyze chemical data has given rise to the analytical study of biological systems, known as "bioinformatics". One facet of bioinformatics is using machine learning (ML) technology to detect multivariable trends in various cases. Amongst the most pressing cases is predicting blood-brain barrier (BBB) permeability. The development of new drugs to treat central nervous system disorders presents unique challenges due to poor penetration efficacy across the blood-brain barrier. In this research, we aim to mitigate this problem through an ML model that analyzes chemical features. To do so: (i) An overview into the relevant biological systems and processes as well as the use case is given. (ii) Second, an in-depth literature review of existing computational techniques for detecting BBB permeability is undertaken. From there, an aspect unexplored across current techniques is identified and a solution is proposed. (iii) Lastly, a two-part in silico model to quantify likelihood of permeability of drugs with defined features across the BBB through passive diffusion is developed, tested, and reflected on. Testing and validation with the dataset determined the predictive logBB model's mean squared error to be around 0.112 units and the neuroinflammation model's mean squared error to be approximately 0.3 units, outperforming all relevant studies found.

Scientists have used artificial intelligence (AI) to create a drug regime for children with a type of deadly brain cancer, where survival rates have not improved for 50 years. Diffuse intrinsic pontine glioma (DIPG) is a rare and fast-growing type of brain tumour in children. These types of tumours are difficult to remove surgically because they are diffuse, which means they do not have well-defined borders suitable for operations. A quarter of children with DIPG have a mutation in a gene known as ACVR1, but there are currently no treatments approved to target this mutation. In a new study, scientists at the Institute of Cancer Research, London (ICR), and the Royal Marsden NHS Foundation Trust were able to use AI to discover that combining the drug everolimus with another called vandetanib could enhance vandetanib's capacity to pass through the blood-brain barrier in order to treat the cancer.

One day in March 2010, Isak McCune started clearing his throat with a forceful, violent sound. The New Hampshire toddler was 3, with a Beatles mop of blonde hair and a cuddly, loving personality. His parents had no idea where the guttural tic came from. They figured it was springtime allergies. Soon after, Isak began to scream as if in pain and grunt at his parents and peers. When he wasn't throwing hours-long tantrums, he stared vacantly into space. By the time he was 5, he was plagued by insistent, terrifying thoughts of death. "He would smash his head into windows and glass whenever the word'dead' came into his head. He was trying to drown out the thoughts," says his mother, Robin McCune, a baker in Goffstown, a small town outside Manchester, New Hampshire's largest city.