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Making Drug Discovery faster, better, and cheaper

Artificial Intelligence in drug discovery

AI drug discovery refers to the use of artificial intelligence (AI) techniques to accelerate and improve the process of discovering new drugs.

We are the future of drug discovery

AI can help in various stages of drug discovery, from target identification to lead optimization. By using AI, researchers can analyze large amounts of data more quickly and accurately, potentially leading to faster and more successful drug discovery .

Artificial Intelligence can deliver value in molecule drug discovery in four ways: access to new biology, improved or novel chemistry, better success rates, and quicker and cheaper discovery processes. The technology can address many challenges and constraints in traditional R&D.

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Molecular Property Prediction

Molecular property prediction is the task of predicting the properties of a molecule from its physical structure. This is a challenging task due to the vast chemical space and the lack of supervised experimental data available for training(Few-shot learning with transformers via graph embeddings for molecular property prediction). Effective molecular representation learning is of great importance to facilitate molecular property prediction(Geometry-enhanced molecular representation learning for property prediction).

Molecular Generation

Molecular generation refers to the use of machine learning-based generative models to generate novel molecules with desirable physiochemical and pharmacological properties from scratch. These models can traverse large and complex chemical space in search of novel compounds that satisfy multiple property constraints(Multi-constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning). This approach has been gaining enormous traction in drug design(MolGPT: Molecular Generation Using a Transformer-Decoder Model)

Drug Repurposing

Drug repurposing, also known as drug repositioning, reprofiling or re-tasking, is a strategy for identifying new uses for approved or investigational drugs that are outside the scope of the original medical indication1. Given the high attrition rates, substantial costs and slow pace of new drug discovery and development, repurposing of ‘old’ drugs to treat both common and rare diseases is increasingly becoming an attractive proposition because it involves the use of de-risked compounds, with potentially lower overall development costs and shorter development timelines(Drug repurposing: progress, challenges and recommendations).

Our Team

Xiu Cheng

Ph.D in Machine Leaning

Martin Xie

Business Intelligence Expert

GanLong Wang

Ph.D in Engineering

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+64 20 40096154

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