Drug Discovery
Drug discovery focuses on identifying and studying molecules that can safely modulate disease processes, aiming to develop medicines that improve patients' quality of life. Advanced technologies drive innovation in drug discovery, making processes faster, more precise, and cost-effective.
These include automation and lab robotics to enable High-Throughput Screening (HTS), and Artificial Intelligence (AI) and Machine Learning (ML) to analyze large datasets to identify patterns, predict drug-target interactions, and optimize lead compounds.
Stages of the Drug Discovery Process
The drug discovery process has five stages:
- Target Discovery: Studying disease biology and identifying molecular targets for intervention.
- Hit Identification: Finding molecules with significant activity on disease biology or the molecular target.
- Hit Confirmation: Further studies of the identified hits
- Hit-to-Lead: Developing lead molecules with acceptable specificity and selectivity based on hits.
- Lead Optimization: Choosing optimized leads for Good Laboratory Practice (GLP)-compliant studies needed for regulatory approval and clinical trials.
The compound then proceeds to preclinical and clinical stages.
How Hamilton Supports your Drug Discovery Workflows
Hamilton products can be used to run most of the methods shown in the table below, particularly in the steps of method setup, sample preparation and sample storage.
1. Target Discovery
Studies of the disease biology and potential molecular targets for intervention
Most common Methods
- Genomics – Polymerase Chain Reaction (PCR), Next-Generation Sequencing (NGS), RNA interference (RNAi), gene knockouts (e.g. CRISPR)
- Proteomics – Liquid Chromatography–Mass Spectrometry (LC-MS)
- Metabolomics – Gas Chromatography / Liquid Chromatography–Mass Spectrometry (GC/LC-MS)
- Structural biology – Nuclear Magnetic Resonance (NMR), X-ray crystallography
- Microscopy, Fluorescence-Activated Cell Sorting (FACS)
- Transgenic animal models
2. Hit Identification
Identification of molecules that show significant activity on the disease biology or molecular target
Most common Methods
- Compound management based on existing compound libraries or newly generated chemicals, or biologics
- High-Throughput Screening (HTS) – e.g., binding assays, protein-protein interactions (Half Maximal Effective Concentration/Half Maximal Inhibitory Concentration (EC/IC50)), cell-based phenotypic readouts (e.g. reporter gene)
3. Hit Confirmation
Further studies of the identified hits
Most common Methods
- Mode of action (MoA)
- Target binding assays (EC/IC50)
- Cellular mechanistic biology studies
- Solubility & stability tests
- Selectivity assays
- Initial cytotoxicity (e.g., MTT, ATP-based assays)
4. Hit-to-Lead
Generation of lead molecules that have acceptable specificity and selectivity for the target, based on the identified hits
Most common Methods
Structure–Activity Relationship (SAR) studies (medicinal chemistry modifications – small molecules; site-directed mutagenesis – biologics)
Early Drug Metabolism and Pharmacokinetics (DMPK), in vitro Absorption, Distribution, Metabolism, and Excretion (ADME) on series/prototype compounds:
- Solubility
- Permeability (Caco-2, Parallel Artificial Membrane Permeability Assay (PAMPA))
- Cytochrome P450 phenotyping
- Plasma protein binding
- Lipophilicity (LogP/D), solubility, and chemical stability
- Microsomal & hepatocyte stability
- Cytotoxicity & genotoxicity testing
5. Lead Optimization
Selection of optimized leads that will enter the GLP-compliant studies required to support regulatory approval for initiating clinical trials
- In vivo Pharmacokinetics & Pharmacodynamics (PK/PD, e.g., tissue distribution, bioavailability)
- In vivo efficacy studies (e.g., xenografts, infection models)
- Toxicology & safety studies (hERG Assays)
Hamilton offers a variety of solutions supporting drug discovery workflows, including liquid handlers, automated sample management systems tailored to a range of temperature needs (ambient, +4°C, -20°C, and -80°C), and manual pipettes.
Some of our liquid handlers include standardized assay-ready workstations optimized for specific applications such as genomics, LC-MS sample preparation, pre-analytics & primary sample processing, and cell maintenance. Additionally, our low-volume liquid handlers are designed specifically for precision in dose-response assays, helping researchers achieve accurate and reproducible results.
Explore our Drug Discovery Solutions and Applications in Detail
Automated workstation
BIOSPOT® Workstation
Assay Ready Workstations
NGS STARlet
Assay Ready Workstations
NGS STAR Line
Assay Ready Workstations
NGS STAR V
-80°C Sample Storage
BiOS
-80°C Sample Storage
SAM HD Pro
Ambient, +4°C, -20°C Sample Storage
Verso
Ambient, +4°C, -20°C Sample Storage
Verso Q-Series
Syringe Applications
GC Autosampler Syringes
Syringe Applications
Manual GC and HPLC Syringes
Good to Know About Drug Discovery Workflows
This section provides a selection of additional resources related to the application described on this page. It includes helpful articles, videos, and blogs that offer deeper insights into the topic.
Hamilton Products in Action:
A collection of videos showcasing Hamilton products in use, providing practical insights into their functionality and benefits.
Solutions for the top 3 Challenges for Drug Discovery Workflows
High-Throughput Screening (HTS) Bottlenecks
Challenge: High-throughput screening requires testing thousands to millions of compounds against a target quickly, a task that is often impractical with manual methods.
Solution: Hamilton automated liquid handlers address these challenges, allowing rapid and accurate reagent and compound dispensing. Additionally, our liquid handlers can be integrated with Hamilton’s automated sample management systems and third-party systems for seamless workflows that include real-time data acquisition tools for faster hit identification and decision-making.
Compound Management and Traceability
Challenge: Managing large compound libraries demands meticulous tracking to prevent cross-contamination and sample degradation, which can be overwhelming with manual systems.
Solution: Hamilton automated liquid handlers and sample management systems: i) enable precise sample handling and storage with automated retrieval systems; ii) integrate barcoding for efficient tracking, and (iii) leverage software to maintain detailed records of compound use and storage conditions, ensuring full traceability and regulatory compliance.
Low-Volume Dispensing for Miniaturized Assays
Challenge: Modern assays are often so costly that they require miniaturization, necessitating precise dispensing of low volumes, which are impossible to achieve manually.
Solution: Hamilton offers a dedicated liquid handler for low-volume, non-contact dispensing that can dispense as low as 2 nl. Moreover, our Microlab STAR, STAR V, and VANTAGE platforms can be equipped with our MagPip channels, providing jet dispensing down to 750 nl for high-precision applications.
Innovation and Trends in Drug Discovery
The following automated systems, innovations, and trends are reshaping drug discovery, making it more efficient, precise, and aligned with the demands of modern healthcare. By leveraging these advances, researchers can address complex diseases, improve treatment outcomes, and reduce development timelines
- Automation reduces the time and resources needed for tasks like HTS and compound handling.
- Miniaturized assays, microfluidics, and acoustic liquid handling enhance precision while reducing reagent consumption.
- Fully integrated robotic laboratory workflows enhance reproducibility and scalability.
- AI and ML streamline automated drug discovery by analyzing vast datasets to:
- Identify novel drug candidates.
- Predict drug-target interactions.
- Optimize chemical structures for better efficacy and safety.
- Algorithms accelerate virtual screening and molecule design, reducing discovery timelines.
- Advances in genomics and proteomics enable the development of treatments tailored to individual genetic profiles.
- Targeted therapies, such as monoclonal antibodies or small molecules, act on specific biomarkers, improving treatment outcomes with fewer side effects.
- Precision oncology is a leading example, where therapies are designed based on cancer patients' unique molecular profiles.
- Monoclonal Antibodies (mAbs) are at the forefront, with applications in oncology, autoimmune diseases, and infectious conditions.
- Advances such as bispecific antibodies and Antibody-Drug Conjugates (ADCs) are pushing the boundaries of biologics, improving precision and efficacy.
- Gene and cell therapies, including Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and Chimeric Antigen Receptor T-cell (CAR-T), advance cancer treatment by repairing faulty genes and reprogramming immune cells.
- Immunotherapies leverage the body’s immune system to fight diseases like cancer and autoimmune disorders. Innovations like checkpoint inhibitors, cancer vaccines, and T-cell therapies are leading innovations in this space.
- The success of mRNA vaccines (e.g., COVID-19 vaccines) has catalyzed interest in RNA-based drugs. RNA interference (RNAi) and Antisense Oligonucleotides (ASOs) are being used to silence disease-causing genes.
- Biosimilars are cost-effective alternatives to biologics, expanding access to advanced therapies.
- Techniques like Cryogenic Electron Microscopy, X-ray crystallography, and Nuclear Magnetic Resonance (NMR) help visualize drug-target interactions at the atomic level.
- Computational modeling and molecular dynamics simulate binding events, enabling rational drug design.
- Structure-based approaches are central to developing drugs for a wide range of challenging targets, like protein-protein interactions.
- Organoids replicate human tissues more precisely than 2D cultures, offering deeper insights into disease mechanisms.
- 3D models provide better insights into disease mechanisms and drug responses, reducing reliance on animal models.
- Genomics, transcriptomics, proteomics, and metabolomics generate high-resolution insights into disease mechanisms.
- These technologies identify novel drug targets, biomarkers, and mechanisms of resistance.
- Multi-omics approaches integrate data for a holistic understanding of complex diseases.
- Repurposing approved drugs for new indications saves time and costs compared to developing new molecules.
- Advances in AI and big data are identifying hidden therapeutic potentials in existing drugs.
- Successful examples include sildenafil (originally for hypertension, repurposed for erectile dysfunction) and remdesivir (from Ebola to COVID-19).
What Is the Role of Dose-Response in Drug Discovery?
The dose-response relationship is a cornerstone of pharmacology, toxicology, and drug discovery, illustrating how varying doses of a substance, like drugs, chemicals, or toxins, influence biological systems. Metrics like IC50 (the concentration to inhibit 50% of a biological response) and EC50 (the concentration to achieve 50% of the maximum effect) are calculated through dose-response studies.
These parameters help research and development teams evaluate drug potency, therapeutic windows, and toxicity thresholds. Dose-response assays are essential for determining optimal drug concentrations in clinical trials, shaping treatment protocols, and advancing personalized medicine. This foundational knowledge ensures safer and more effective drug development and usage, improving public health outcomes across diverse conditions.
Drug Discovery Case Studies and Application Notes
Other Drug Discovery Resources
Browse app notes, user guides, specification documents, and more in our Knowledge Center.
Browse app notes, user guides, specification documents, and more in our Knowledge Center.
Browse app notes, user guides, specification documents, and more in our Knowledge Center.
Browse app notes, user guides, specification documents, and more in our Knowledge Center.
Browse app notes, user guides, specification documents, and more in our Knowledge Center.
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