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Related Concept Videos

Associative Learning01:27

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
Ligand Binding Sites02:40

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
Ligand Binding Sites02:40

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Ligand Binding and Linkage00:49

Ligand Binding and Linkage

Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence the...
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence the...

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Related Experiment Videos

Nevermore: Target-Conditioned Protein-Ligand Representation Learning for Multi-Objective Lead Optimization with

Mohammad Saleh Refahi1, Milad Toutounchian2, Bahrad A Sokhansanj1

  • 1Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA 19104, USA.

Biology
|June 25, 2026
PubMed
Summary
This summary is machine-generated.

We developed Nevermore, an AI workflow that prioritizes drug candidates by predicting protein binding and ensuring synthesized molecules are valid. This approach accelerates lead identification for drug discovery.

Keywords:
contrastive representation learningdatabase-grounded retrievalmulti-objective lead optimizationprotein–ligand affinity predictiontarget-conditioned molecular design

Related Experiment Videos

Area of Science:

  • Computational chemistry
  • Artificial intelligence in drug discovery
  • Medicinal chemistry

Background:

  • De novo drug design requires AI to predict protein target engagement and medicinal chemistry criteria.
  • Existing methods often lack grounding in real-world chemical databases for synthesis and modification.

Purpose of the Study:

  • To present Nevermore, an AI-driven workflow for prioritizing drug candidates from large libraries.
  • To integrate target-specific binding prediction with database validity for lead generation.

Main Methods:

  • Utilized a geometry-aware AI model for protein-ligand affinity scoring.
  • Employed sparse integer edits in Morgan fingerprint space for molecular optimization.
  • Retrieved structurally similar, valid compounds from public chemical databases.
  • Performed multi-objective search considering affinity and absorption, distribution, metabolism, excretion, and toxicity (ADMET) proxies.

Main Results:

  • Nevermore successfully prioritized candidate ligands across three distinct targets: Menin, SARS-CoV-2 Mpro, and EGFR.
  • The workflow demonstrated favorable trade-offs between predicted affinity and drug-like properties.
  • Prioritized candidates were anchored to valid compounds in public chemical databases.

Conclusions:

  • Database-grounded fingerprint steering is a practical computational strategy for lead prioritization.
  • Nevermore facilitates the generation of testable molecular hypotheses for drug discovery.
  • Experimental validation is crucial for prioritized AI-generated drug candidates.