Advancing Drug Discovery via Artificial Intelligence.

By H. C. Stephen Chan, Hanbin Shan, T. Dahoun, H. Vogel, Shuguang Yuan. TIPS - Trends in Pharmacological Sciences, 2019

480 citations recorded by the source corpus.

Drug discovery and development are among the most important translational science activities that contribute to human health and wellbeing. However, the development of a new drug is a very complex, expensive, and long process which typically costs 2.6 billion USD and takes 12 years on average. How to decrease the costs and speed up new drug discovery has become a challenging and urgent question in industry. Artificial intelligence (AI) combined with new experimental technologies is expected to make the hunt for new pharmaceuticals quicker, cheaper, and more effective. We discuss here emerging applications of AI to improve the drug discovery process.

Subfields: Biophysics, Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c9546fc91e167fcd78476a6a1bb99232333ef075

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Lauv Patel
2. David Wayne Ussery
3. Emilie Kaufmann
4. Xiuzhen Huang
5. T. Shukla
6. Shanzhi Wang

In Molecular Biology

1. Lauv Patel
2. David Wayne Ussery
3. Xiuzhen Huang
4. T. Shukla
5. Shanzhi Wang

Citations in the corpus

• Artificial intelligence in drug discovery and development (2020, methodology): “Drug design algorithms, such as cou-lomb matrices and molecular fingerprint recognition, consider the physical, chemical, and toxicological profiles to select a lead compound [23].”
• DrugBank 6.0: the DrugBank Knowledgebase for 2024 (2023, background): “…contributions to early-stage drug discovery ranging from target identification to lead identification / optimization to drug repurposing to chemical synthesis optimization, and even to the prediction of important drug properties such as efficacy, toxicity and drug–drug interactions ( 1–3 ).”
• Artificial intelligence in drug development (2025, unknown): “246), we can be judiciously optimistic about AI’s promise for accelerating drug development and benefiting human health.”
• Machine learning applications in drug development (2019, background): “Authors of [21] estimate their use would shrink the drug candidate identification phase from a few months to one year.”
• Machine Learning Methods in Drug Discovery (2020, methodology): “The MCTS technique is ideal for this purpose because of the simulation’s ability to perform random continuous step searches without branching until optimal conditions and solutions are met [82,83].”
• Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector (2023, unknown): “As a result, the healthcare industry is attempting to integrate new IT-based technologies and processes that may cut costs and give solutions to these expanding difficulties [1].”