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Related Concept Videos

Drug Discovery: Overview01:26

Drug Discovery: Overview

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
Preclinical Development: Overview01:28

Preclinical Development: Overview

Preclinical development consists of a series of tests that ensure the safety and efficacy of a new therapeutic compound before it is tested in humans. There are four main phases to this process. First, safety pharmacology tests are conducted to ensure the drug does not produce any acutely harmful effects. These tests examine parameters such as bronchoconstriction, cardiac dysrhythmias, blood pressure changes, and ataxia. Next, preliminary toxicological testing is performed to determine the...
Clinical Trials: Overview01:11

Clinical Trials: Overview

Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence its...
In Vitro Drug Release Testing: Overview, Development and Validation01:10

In Vitro Drug Release Testing: Overview, Development and Validation

In vitro dissolution and drug release tests assess how quickly and how much of a drug is released from its dosage form into an aqueous medium under standardized laboratory conditions. These tests are essential tools in pharmaceutical development and quality assurance, offering insight into the drug's performance before clinical use.During formulation development, dissolution testing identifies incomplete or inconsistent drug release issues. It also supports decisions on selecting the optimal...
Drug Administration and Therapy Phases: Overview01:26

Drug Administration and Therapy Phases: Overview

Drugs, the chemical agents used in diagnosing, treating, or preventing diseases, undergo a four-phase process of development: pharmaceutic, pharmacokinetics, pharmacodynamics, and therapeutic.
The pharmaceutical phase focuses on leveraging the physicochemical properties of the drug to design and manufacture an effective product. Variants include orally administered tablets or capsules, topical creams or ointments, and parenteral-delivery solutions or emulsions.
The pharmacokinetic phase...

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Artificial Intelligence in drug discovery and development: current landscape, challenges, and future perspectives.

Attila A Seyhan1, Claudio Carini2

  • 1Laboratory of Translational Oncology and Experimental Cancer Therapeutics, Warren Alpert, Medical School, Brown University, Providence, RI, United States; Department of Pathology and Laboratory Medicine, Warren Alpert Medical School, Brown University, Providence, RI, United States; Joint Program in Cancer Biology, Brown Health System and Brown University, Providence, RI, United States; Legorreta Cancer Center at Brown University, Providence, RI, United States.

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Summary

Artificial intelligence (AI) is revolutionizing drug discovery by accelerating exploration of chemical spaces and optimizing therapeutic candidates. Challenges remain in data quality and experimental validation for AI-designed drugs to reach market approval.

Keywords:
Artificial intelligenceand ethical and regulatory implicationsclinical trialsdrug repurposingdrug/target identification and developmentgenerative AI

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Area of Science:

  • Computational chemistry and pharmacology
  • Biotechnology and pharmaceutical sciences
  • Machine learning in healthcare

Background:

  • Traditional drug discovery is hampered by long timelines, high costs, and significant attrition rates.
  • Artificial intelligence (AI) offers transformative potential to overcome these limitations.
  • Generative modeling and advanced AI platforms are expanding the scope of drug research.

Purpose of the Study:

  • To review the evolution of AI approaches in drug discovery and development.
  • To highlight key challenges and opportunities in AI-driven therapeutic innovation.
  • To examine AI's impact across the R&D pipeline, from target identification to clinical decision-making.

Main Methods:

  • Analysis of recent advances in AI, including generative modeling, scaffold-aware design, 3D molecular design tools (e.g., AlphaFold, MoleR, PocketCrafter), single-cell foundation models, and large language models (LLMs).
  • Integration of AI into preclinical and clinical workflows, such as adaptive trial design and AI-driven drug repurposing.
  • Examination of regulatory landscape (e.g., U.S. FDA approvals) and clinical progression of AI-originated candidates.

Main Results:

  • AI accelerates exploration of chemical and biological spaces, improving molecular modeling and candidate optimization.
  • Emerging AI platforms enhance target identification, lead optimization, phenotypic screening, and precision biology.
  • Several AI-originated drug candidates are in clinical development, demonstrating translational impact.
  • AI acts as a collaborative tool, uncovering novel designs and informing downstream development.

Conclusions:

  • AI is a powerful collaborative partner in drug discovery, enhancing efficiency, scalability, and cost-effectiveness.
  • Broader AI impact hinges on high-quality multimodal data, robust regulatory/ethical frameworks, and acknowledging methodological limits.
  • AI is poised to significantly shape the future of data-driven therapeutic innovation.