Goto

Collaborating Authors

AI helps with drug discovery

#artificialintelligence

Drug-target interaction is a prominent research area in drug discovery, which refers to the recognition of interactions between chemical compounds and the protein targets. Chemists estimate that 1060 compounds with drug-like properties could be made--that's more than the total number of atoms in the Solar System, as an article reported in the journal Nature in 2017. Drug development, on average, takes about 14 years and costs up to 1.5 billion dollars. During the journey of drug discovery in this vast "galaxy," it is apparent that traditional biological experiments for DTI detection are normally costly and time-consuming. Prof. Hou Tingjun is an expert in computer-aided drug design (CADD) at the Zhejiang University College of Pharmaceutical Sciences.


Target-aware Molecular Graph Generation

arXiv.org Artificial Intelligence

Generating molecules with desired biological activities has attracted growing attention in drug discovery. Previous molecular generation models are designed as chemocentric methods that hardly consider the drug-target interaction, limiting their practical applications. In this paper, we aim to generate molecular drugs in a target-aware manner that bridges biological activity and molecular design. To solve this problem, we compile a benchmark dataset from several publicly available datasets and build baselines in a unified framework. Building on the recent advantages of flow-based molecular generation models, we propose SiamFlow, which forces the flow to fit the distribution of target sequence embeddings in latent space. Specifically, we employ an alignment loss and a uniform loss to bring target sequence embeddings and drug graph embeddings into agreements while avoiding collapse. Furthermore, we formulate the alignment into a one-to-many problem by learning spaces of target sequence embeddings. Experiments quantitatively show that our proposed method learns meaningful representations in the latent space toward the target-aware molecular graph generation and provides an alternative approach to bridge biology and chemistry in drug discovery.


Ping An Makes Breakthrough in Artificial Intelligence-Driven Drug Research

#artificialintelligence

Research by Ping An Healthcare Technology Research Institute and Tsinghua University has led to a promising deep learning framework for drug discovery, announced Ping An Insurance (Group) Company of China, Ltd. (hereafter "Ping An" or the "Group", HKEX: 2318; SSE: 601318). The findings were published in "An effective self-supervised framework for learning expressive molecular global representations to drug discovery" in Briefings in Bioinformatics, a peer-reviewed bioinformatics journal. It marks a major technology breakthrough for the Group in the field of AI-driven pharmaceutical research. Drug discovery can take 10 to 15 years from invention to market. It can take a large number of experiments, with significant costs and high failure rates.


AI for Pharma R&D - Creating Anti-cancer Drugs Faster, Reducing Process from Years to Days - insideBIGDATA

#artificialintelligence

The costs and process of developing anti-cancer drugs has been an extreme challenge for decades. Today one company, AccutarBio, is harnessing the power of AI to accelerate drug discovery and reform the current "hit-to-lead" drug discovery scheme. The company recently received $15 million in funding (including money from Chinese AI/facial recognition company YITU) and is now partnering with Amgen. AccutarBio is proud of the dramatic improvements the company's hybrid approach (combining computation design and experimental validation) has made in radically speeding up the drug discovery process. The video below demonstrates how the company's Orbital docking and virtual screen works.


DeepPurpose: a Deep Learning Based Drug Repurposing Toolkit

arXiv.org Machine Learning

With a few lines of code, DeepPurpose generates drug candidates based on aggregating five pretrained state-of-the-art models while offering flexibility for users to train their own models with 15 drug/target encodings and 50 novel architectures. We demonstrated DeepPurpose using case studies, including repurposing for COVID-19 where promising candidates under trials are ranked high in our results. Drug repurposing is about investigating existing drugs for new therapeutic purposes which can potentially speed up drug development 1 . With a large number of existing drugs, it is important to quickly and accurately identify promising candidates for new indications. Especially in facing COVID-19 pandemic today, drug repurposing become particularly relevant as a potentially much faster way to discover effective and safe drugs for treating COVID-19. Deep learning has recently demonstrated its superior performance than classic methods to assist computational drug discovery 2, 3, thanks to its expressive power in extracting, processing and extrapolating patterns in molecular data.