Drug Discovery Pro

11/05/2026

🚀 Workshop Announcement 🚀

C-SAR: A New Strategy for Accelerating Structure Development

📅 Date: Sat. May 23, 2026
🕒 Time: 3:00 PM – 6:00 PM

Join us for an exciting scientific workshop introducing C-SAR, an innovative strategy designed to accelerate structure development and redefine modern Structure–Activity Relationship (SAR) analysis.

This workshop will provide participants with a unique opportunity to explore cutting-edge methodologies in chemical data curation, matched molecular pair (MMP) preparation, and advanced C-SAR analysis using selective HDAC6 inhibitors as a practical case study.

🔬 Workshop Highlights
-Introduction to the new concept of C-SAR
-Key differences between C-SAR and classical SAR methodologies
-Advanced chemical data curation practices
-Preparation and validation of MMP datasets
-Modern data cleaning and manipulation techniques
-Development of data diversity indexing
-Innovative approaches for C-SAR highlight extraction
-Validation of the C-SAR concept

🎯 Learning Objectives

Participants will:

Understand the definition and principles of C-SAR
Differentiate between C-SAR and traditional SAR approaches
Learn how to prepare and analyze datasets for C-SAR studies
Explore practical solutions to common C-SAR challenges
📚 Learning Outcomes

By the end of the workshop, participants will gain knowledge in:
✔ Modern data curation practices
✔ Advanced data manipulation techniques
✔ Innovative data cleansing strategies
✔ Diversity index development
✔ Cutting-edge C-SAR extraction methods
✔ Validation strategies for C-SAR studies

🗓 Workshop Agenda

3:00 – 3:30 PM | Speed-up networking with participants
3:30 – 3:40 PM | Definition of the new terminology C-SAR
3:40 – 3:50 PM | Difference between C-SAR and classical SARs
3:50 – 4:00 PM | Chemical data curation of selective HDAC6 inhibitors
4:00 – 5:00 PM | Preparation and specifications of MMPs of selective HDAC6
5:00 – 5:30 PM | C-SAR concept validation
5:30 – 6:00 PM | Q&A Session

🌟 Whether you are a researcher, graduate student, medicinal chemist, or data scientist, this workshop will offer insightful perspectives on the future of structure development and SAR innovation.

📢 Don’t miss this opportunity to explore the next generation of SAR methodologies and connect with researchers passionate about computational and medicinal chemistry innovation!

Registration is here: https://forms.gle/dKqgA5frFBur15h2A

12/04/2026

Smarter with PROTAC means trustful results in a short time:

Compounds Library Compound library Compound Library The compound or fragment library designed in PROTAC is an efficient collection of diverse chemical compounds utilized in drug discovery and other areas of chemistry like high-throughput screening to identify active compounds, or “hits,” that ta...

08/04/2026

🧪 A 7-Year Lesson in Keap1–Nrf2 Drug Discovery: When Potency Misleads Medicinal Chemistry

The story of Keap1–Nrf2 signaling pathway inhibitors offers a powerful reminder for medicinal chemists working in modern Design–Make–Test–Analyze (DMTA) cycles.

It begins with a promising discovery.

🔬 2013 — A powerful hit appears https://www.sciencedirect.com/science/article/abs/pii/S0968089613003453?via%3Dihub

A high-throughput screening campaign from the Evotec Lead Discovery Library identified a naphthalene-based hit as an inhibitor of Kelch-like ECH-associated protein 1, disrupting the interaction with Nuclear factor erythroid 2–related factor 2.

The molecule looked excellent on paper:

But the real story had only just begun.

⚙️ 2014 — Structure-guided optimization

Researchers led by Jiang et al. modified the scaffold into a dicarboxylate derivative, achieving nanomolar potency through structure-based design.

• IC₅₀ = 25 nM
• Strong binding
• Clear biochemical activity
https://pubs.acs.org/doi/10.1021/jm5000529
Encouraged by the activity, the group continued exploring the scaffold for diagnostic probes.

The chemistry looked promising.

Yet a deeper issue remained hidden.

🧬 2018–2020 — The optimization struggle
https://pubs.acs.org/doi/10.1021/acs.jmedchem.8b01133
The group of Terry Moor attempted to turn the hit into a drug-like molecule.

The objectives were clear:

• Improve metabolic stability
• Enhance solubility
• Achieve cellular activity

By 2020, these goals were partially achieved.

But there was a price.

The potency dropped to IC₅₀ = 73 nM.
https://pubs.acs.org/doi/abs/10.1021/acs.jmedchem.9b01074
Despite seven years of work across two groups, the naphthalene chemotype proved extremely difficult to develop without sacrificing activity.

🧭 2022 — A different strategy
https://pubs.acs.org/doi/10.1021/acs.jmedchem.2c00830

Instead of further forcing the original scaffold, Bach et al. explored chemical space through virtual screening.

They identified a fluorene-based hit — a completely different chemotype.

Using fragment growing, they optimized the structure to achieve:

• Ki = 0.28 μM
• Strong binding to Keap1

And more importantly:

A developable scaffold.

🚀 2024 — The breakthrough
https://pubs.acs.org/doi/10.1021/acs.jmedchem.4c01221

The fluorene scaffold was fully optimized.

The result:

• 85-fold potency improvement
• High metabolic stability
• Strong cellular activity
• No compromise in potency

After years of struggle, the problem was not optimization.

The problem was the starting chemotype.

💡 The Medicinal Chemistry Lesson

In drug discovery, potency can be seductive.

But potency alone does not define a good hit.

The Keap1–Nrf2 story shows that:

✔ A highly potent hit may still be chemically non-developable
✔ Forcing optimization on the wrong scaffold can waste years
✔ Exploring chemical space can unlock entirely new optimization trajectories

Sometimes the fastest DMTA cycle is not optimizing the hit.

It is replacing it.

🧠 For medicinal chemists:

Before committing to a hit, always ask:

Is this scaffold truly developable?

Because in modern drug discovery, the quality of the starting chemotype often determines the success of the entire program.

07/04/2026

Another round of the program will start May 9, 2026.

🚀 Now Open for Registration!

https://drugdiscoverypro.com/product/drug-discovery-cycle-for-non-pharmacy-based-scholars/

🎓 Training Program Announcement

Drug Discovery Cycle for Non-Pharmacy-Based Scholars

Are you a researcher from a non-pharmacy background who wants to enter the world of modern drug discovery?

This comprehensive training program is designed to guide scholars through the complete drug discovery cycle — from target analysis to lead optimization — using integrated bioinformatics, cheminformatics, and computational drug design tools.

🔬 Program Overview

The Drug Discovery Cycle consists of essential, interconnected stages required for successful hit identification and lead discovery. Limiting the protocol to isolated steps often closes doors to innovation and restricts problem-solving strategies.

This program presents a robust, structured flowchart that maximizes project success and strategic decision-making.

🧬 Module 1: Bioinformatics

Understanding the biological target:

Target protein structure (3D structure)

Active site identification

Activity regulation

Isoforms analysis

Annotation score evaluation

Sequence length assessment

⚗️ Module 2: Cheminformatics

Understanding the chemical ligand:

Chemical structure analysis

Database search strategies

Physicochemical properties calculation

Statistical analysis of PK parameters

Structure similarity search

Chemical library enumeration

Evolutionary chemical library design

Fuzzy scoring approaches

💻 Module 3: Virtual Screening of Designed Libraries

Molecular docking of evolved chemical libraries

Filtration and prioritization of diverse hits

🧪 Module 4: Chemical Accessibility

One of the most critical selection criteria in hit prioritization:

Synthetic feasibility evaluation

Practical accessibility assessment

Strategic compound selection

🔎 Module 5: Structure Validation

The most challenging and costly stage of the cycle:

Data interpretation and outlier judgment

Structural verification strategies

Rapid direction-setting for optimization

🚀 Module 6: Lead Optimization

A crucial stage in transforming hits into drug candidates through iterative design and testing.

Key optimization aspects:

Potency – Enhancing biological activity

Selectivity – Reducing off-target interactions

Pharmacokinetics – Optimizing ADME properties

Safety – Minimizing toxicity

Chemical Stability – Ensuring stability in physiological and storage conditions

Duration – Improving therapeutic persistence

This stage integrates biological assays, computational modeling, and medicinal chemistry principles.

📅 Program Details

Start Date: April 4, 2026

Duration: 10 weeks

Session Format: Online

Session Length: 3–4 hours per session

Total Training Hours: 35–40 hours

Level: Basic Introductory → Advanced Applications

🎯 Prerequisites

Background in Organic Chemistry and Biology (Mandatory)

Computer Science knowledge (Preferred but not mandatory)

💰 Program Fee

10,000 EGP

✨ Discount Policy Available for Group Registration

📝 Program Timeline

Registration https://forms.gle/YtqHDsc1NhrMLTxi9

Contact to confirm participation

Scheduling of sessions

Technical support provided for software installation

🌟 Why Join This Program?

✔ Designed specifically for non-pharmacy scholars
✔ Covers the full drug discovery pipeline
✔ Integrates computational and practical perspectives
✔ Structured and innovation-oriented methodology
✔ Hands-on guided workflow

📩 Secure Your Spot Now!

Seats are limited to maintain high-quality interaction and mentorship.

04/04/2026

🧪 From a weak hit in 1989 to a clinical candidate in 2019.

Thirty years of medicinal chemistry.

The story of Navoximod (GDC-0919) may be one of the most instructive examples of patience in modern drug discovery https://pubs.acs.org/doi/10.1021/acs.jmedchem.9b00662.

It began in 1989, when a high-throughput screening campaign identified a small fragment-like molecule:

4-phenylimidazole

Its activity against the immune checkpoint enzyme Indoleamine 2,3-dioxygenase 1 (IDO1) was modest:

IC₅₀ = 28 µM

For many programs, this would have been the end of the story.

But the research team chose a different path.

They began a three-decade medicinal chemistry journey.

Step by step, the molecule started teaching them.

🔬 Early SAR insights

• Adding a 2′-OH group improved potency 16-fold
• Introducing 3′-F and 5′-Cl boosted activity even further
• But potency came with a price: metabolic instability

Then the team realized something deeper.

The molecule was too flexible.

So they introduced molecular rigidification, transforming the scaffold into an imidazoisoindole system.

This structural constraint dramatically improved binding to the heme iron inside the IDO1 active site.

And then the crucial lessons emerged:

• Stereochemistry-controlled potency
• Hydrophobic interactions near Phe226 were essential
• Cellular activity required careful polarity control
• Metabolism had to be engineered away from CYP3A4 inhibition

Through dozens of iterations, the team balanced:

⚖️ potency
⚖️ cellular activity
⚖️ metabolic stability
⚖️ pharmacokinetics
⚖️ selectivity

Eventually, the optimized compound emerged:

Navoximod

• hIDO1 IC₅₀ = 0.028 µM
• Cellular IC₅₀ = 0.075 µM

A ~1000-fold improvement from the original hit.
https://chemistry-europe.onlinelibrary.wiley.com/doi/full/10.1002/cmdc.202100253

But what makes the 2019 report remarkable is not just the chemistry.

It is the humility.

The authors carefully describe:

• failed ideas
• compromises
• unexpected observations
• lessons learned along the way

They do not present the discovery as brilliance.

They present it as persistent learning from molecules.

And perhaps that is the most important lesson of all.

💡 Drug discovery rarely happens in a single insight.

Sometimes it takes 30 years of listening to the chemistry.

04/04/2026

03/04/2026

🧬 When Chemical Data Became the Engine of SAR

In 1962, John G. Topliss introduced medicinal chemists to a systematic way of thinking about structure–activity relationships (SAR) https://pubs.acs.org/doi/10.1021/jm01237a009.

His idea was simple but powerful: modify a parent structure step-by-step and let the biological data guide the next substitution https://pubs.acs.org/doi/10.1021/jm00280a002.

Soon after, Corwin Hansch quantified substituent effects through physicochemical parameters such as σ (electronic), π (hydrophobic), and Es (steric) — turning chemical intuition into data-driven decision making https://www.nature.com/articles/194178b0.

This synergy produced the famous Topliss decision tree, followed later by the Topliss Batchwise Scheme (TBS) — frameworks designed to optimize analogs while saving time, experimental effort, and computational resources https://pubs.acs.org/doi/10.1021/jm00214a001.

⚗️ For decades, this approach shaped medicinal chemistry:
Systematic substituent exploration
Efficient SAR generation
Guided lead optimization

But science evolves with data.

Large-scale chemical datasets and structural biology insights have revealed important limitations:
🔹 The Topliss framework assumes a stable parent chemotype (often an unfused benzene ring).
🔹 SAR derived from one chemotype does not always translate to others.
🔹 The approach struggles with molecular transformation when scaffold changes are required.
🔹 Recent studies suggest the scheme performs better for enzyme targets than for membrane receptors, where binding-site structure and X-ray complex data become critical https://pubs.acs.org/doi/10.1021/acs.jcim.7b00195.

In other words:
➡️ Early SAR was guided by chemical logic.
➡️ Modern SAR is guided by chemical data at scale.
Today, medicinal chemistry is entering a new era where:
👊 Large chemical datasets
👊 Structural biology
👊 Computational modeling
🛶 AI-driven SAR analysis

Work together to reveal how molecular structure truly drives biological activity.
The legacy of Topliss remains profound — but the future of SAR lies in integrating classical medicinal chemistry logic with modern data-driven frameworks.

🚀 The next breakthrough molecules may emerge not only from modifying structures…
But from transforming chemotypes using multidimensional chemical data.

In a recent study, a new C-SAR paradigm has been developped for converting chemotypes utilizing multidimensional chemical data, allowing us to change the chemical structure without worrying about activity loss https://www.sciencedirect.com/.../abs/pii/S0010482525005207.

31/03/2026

🧪 A Powerful Lesson for the Scientific Community: The Story of Linrodostat
In drug discovery, breakthroughs often come not only from experiments but from scientific curiosity and intellectual honesty.

A fascinating example comes from the discovery of Linrodostat (BMS-986205), a highly potent inhibitor of Indoleamine 2,3‑dioxygenase 1 (IDO1).
Originally developed by Flexus Biosciences and later licensed to Bristol‑Myers Squibb, Linrodostat attracted significant attention because of its extraordinary cellular potency (EC50 ≈ 0.5 nM) and its irreversible su***de inhibition mechanism.

In 2018, a study led by John T. Groves and published in Proceedings of the National Academy of Sciences proposed a remarkable pharmacodynamic insight:
IDO1 may exist in two interconverting states
• a heme-bound holo form
• a heme-free apo form
The study suggested that Linrodostat primarily targets the apo form of the enzyme. https://www.pnas.org/doi/10.1073/pnas.1719190115
The reviewer of this publication was Syun‑Ru Yeh.
But the story didn’t end there.

Months later, Yeh and her team conducted their own structural investigation to explore the binding mechanism in greater detail. Instead of dismissing the earlier work, they tested the hypothesis experimentally.

Their crystallographic studies revealed something remarkable.
Rather than binding directly to an apo enzyme, Linrodostat appears to follow a multi-step binding trajectory in which it gradually displaces the heme group from the holo enzyme.

Three structural snapshots captured this process:
• Extended conformer – initial binding near the pocket entrance
(PDB 6DPR)
• Kinked conformer – entry into the heme pocket after destabilizing heme coordination
(PDB 6DPQ)
• Bent conformer – final stabilized binding mode in the Si site
(PDB 6MQ6)
https://pubs.acs.org/doi/10.1021/jacs.8b07994
The earlier structure from Groves’ study (PDB 6AZV) had already shown the enzyme without the heme cofactor, but Yeh’s work revealed how the inhibitor actually gets there https://chemistry-europe.onlinelibrary.wiley.com/doi/full/10.1002/cmdc.202100253.

Their findings beautifully illustrated:
🔬 conformational selection followed by
🔬 induced fit stabilization

🌍 But the real lesson here goes beyond structural biology.

Syun-Ru Yeh had already reviewed and approved the original manuscript.
Yet instead of assuming the story was complete, she pursued the question further in her own lab.

And when the data suggested a different mechanistic interpretation, she presented it with remarkable scientific humility—not to contradict colleagues, but to advance understanding.
No arrogance.
No confrontation.
Just better science.

📌 Lesson for researchers
Science progresses when we:
• Stay curious even after peer review
• Test ideas—even those we previously accepted
• Challenge hypotheses with data, not ego
• Communicate disagreements with respect and integrity

True scientific leadership is not about proving others wrong.
It is about moving knowledge forward.
And this story of Linrodostat and IDO1 is a beautiful reminder of that. ✨

30/03/2026

🧪 What if an inactive molecule could become an active drug candidate through strategic chemotype transformation?

For decades, medicinal chemists have relied on classical SAR approaches such as the John G. Topliss tree to optimize compounds. These methods are powerful—but often limited to modifications within a single scaffold.

A recent work introduces a different perspective: C-SAR (Cross-Structure-Activity Relationship).

Instead of focusing only on one parent structure, C-SAR explores repetitive pharmacophoric substitution patterns across diverse chemotypes, revealing activity cliffs that can guide the transformation of inactive molecules into promising active ones.

Using , molecular pair analysis, and docking on datasets targeting Histone Deacetylase 6 (HDAC6), we identified patterns that can help design novel chemotypes beyond the original dataset.

💡 Key idea:
The future of medicinal chemistry may lie not only in optimizing scaffolds, but in learning how activity emerges across different chemotypes.

With high-quality structural data—and potentially AI-driven data imputation—approaches like C-SAR could help expand chemical space and increase the success rate of structure development.

🔬 I encourage researchers to explore this concept within their own datasets and contribute to building a more diverse and innovative molecular landscape.

Let’s push the boundaries of chemical discovery together.



https://www.sciencedirect.com/science/article/abs/pii/S0010482525005207

27/03/2026

🚀 Now Open for Registration!

https://drugdiscoverypro.com/product/drug-discovery-cycle-for-non-pharmacy-based-scholars/

🎓 Training Program Announcement

Drug Discovery Cycle for Non-Pharmacy-Based Scholars

Are you a researcher from a non-pharmacy background who wants to enter the world of modern drug discovery?

This comprehensive training program is designed to guide scholars through the complete drug discovery cycle — from target analysis to lead optimization — using integrated bioinformatics, cheminformatics, and computational drug design tools.

🔬 Program Overview

The Drug Discovery Cycle consists of essential, interconnected stages required for successful hit identification and lead discovery. Limiting the protocol to isolated steps often closes doors to innovation and restricts problem-solving strategies.

This program presents a robust, structured flowchart that maximizes project success and strategic decision-making.

🧬 Module 1: Bioinformatics

Understanding the biological target:

Target protein structure (3D structure)

Active site identification

Activity regulation

Isoforms analysis

Annotation score evaluation

Sequence length assessment

⚗️ Module 2: Cheminformatics

Understanding the chemical ligand:

Chemical structure analysis

Database search strategies

Physicochemical properties calculation

Statistical analysis of PK parameters

Structure similarity search

Chemical library enumeration

Evolutionary chemical library design

Fuzzy scoring approaches

💻 Module 3: Virtual Screening of Designed Libraries

Molecular docking of evolved chemical libraries

Filtration and prioritization of diverse hits

🧪 Module 4: Chemical Accessibility

One of the most critical selection criteria in hit prioritization:

Synthetic feasibility evaluation

Practical accessibility assessment

Strategic compound selection

🔎 Module 5: Structure Validation

The most challenging and costly stage of the cycle:

Data interpretation and outlier judgment

Structural verification strategies

Rapid direction-setting for optimization

🚀 Module 6: Lead Optimization

A crucial stage in transforming hits into drug candidates through iterative design and testing.

Key optimization aspects:

Potency – Enhancing biological activity

Selectivity – Reducing off-target interactions

Pharmacokinetics – Optimizing ADME properties

Safety – Minimizing toxicity

Chemical Stability – Ensuring stability in physiological and storage conditions

Duration – Improving therapeutic persistence

This stage integrates biological assays, computational modeling, and medicinal chemistry principles.

📅 Program Details

Start Date: April 4, 2026

Duration: 10 weeks

Session Format: Online

Session Length: 3–4 hours per session

Total Training Hours: 35–40 hours

Level: Basic Introductory → Advanced Applications

🎯 Prerequisites

Background in Organic Chemistry and Biology (Mandatory)

Computer Science knowledge (Preferred but not mandatory)

💰 Program Fee

10,000 EGP

✨ Discount Policy Available for Group Registration

📝 Program Timeline

Registration https://forms.gle/YtqHDsc1NhrMLTxi9

Contact to confirm participation

Scheduling of sessions

Technical support provided for software installation

🌟 Why Join This Program?

✔ Designed specifically for non-pharmacy scholars
✔ Covers the full drug discovery pipeline
✔ Integrates computational and practical perspectives
✔ Structured and innovation-oriented methodology
✔ Hands-on guided workflow

📩 Secure Your Spot Now!

Seats are limited to maintain high-quality interaction and mentorship.

11/03/2026

https://www.linkedin.com/pulse/discovery-potential-drugs-against-mutant-genes-serious-xmbgf

Cheminformatics has been instrumental in drug discovery, particularly in cancer research. Using computational tools allows scientists to analyze chemical data, predict the behavior of compounds, and identify potential drug candidates faster than traditional methods.