Large-Scale Databases Can be Used for Drug-Target Prediction
The large-scale comparisons of protein-binding sites gave a lot of insights to understand drug specificity. Millions of experimental data on drug-target interactions are available in public databases that can be used for in silico drug-target profiling. Creative Biolabs will mobilize huge network resources to help you predict drug targets with the help of multiple platforms.
We Can Do So Much
Drug discoveries have tuned into the combination of experimental approaches and modern science of computational. Various tools and techniques have been used for target identification, enrichment analysis, and network algorithm. Several in silico bioinformatic methods have been developed and applied.
Single-Target Entity-Based Approaches
-
Chemical Similarity Searching
Chemical similarity search heavily depends on the existence of reference compounds (usually inhibitors) for known targets and therefore on the existence of data for existing drugs or preclinical drug candidates. Biochemical affinities of screening compound collections are another source of information that can be used for in silico target profiling. Such data available from public domains’ databases such as ChEMBL or PubChem were reassembled into drug-target databases to support fast in silico target profiling.
-
Machine Learning (ML) Approach
ML approaches use a supervised learning concept, which “learn” the type of drug-target interactions based on known data. They try to relate classes of compounds with their associated targets.
-
3D Structure-Based Approaches
The method uses 3D structural information concerning the target and/or the ligand bound to its target binding site. Various in silico target profiling techniques are associated with these structure-based approaches.
Compound Set Metric-Based Approaches
-
Similarity Ensemble Approach (SEA)
This approach gives an estimation of the structural distance between two targets: instead of considering a single distance between a pair of inhibitors of target A and target B, a distance distribution profile is calculated between all inhibitors of target A with all inhibitors of target B.
The SEA-metric technique was applied to validate the primary target of a known drug and to infer new cross-reactivity with unexpected targets, which were validated experimentally. This SEA-metric is a general approach that is well suited for compound prioritization after the cell-based screening.
-
Structure-Activity Relationship (SAR) Approach
It maps the 2D or 3D structure in an activity profile previously constructed with a set of known active and inactive compounds for that target. Testing a compound onto a SAR model is equivalent to comparing this compound to a whole set of active and inactive reference inhibitors for a given target.
Given the tremendous growth of bioactivity databases, the use of computational tools to predict protein targets of small molecules has been gaining importance in recent years. Creative Biolabs keeps working hard to provide first-class services to customers all over the world. Our scientists will solve all the problems in drug target prediction for you. If you have any questions, please contact us.
For Research Use Only.
