ProtoQSAR and the Institute of Medicinal Chemistry (IQM-CSIC) sign a collaboration agreement to accelerate drug discovery
ProtoQSAR, a Valencia-based company specialized in computational modeling and chemoinformatics, and the Instituto de Química Médica (IQM-CSIC), a research center of the Consejo Superior de Investigaciones Científicas (CSIC), have signed a strategic collaboration agreement to accelerate the design and optimization of new therapeutic candidates through advanced in silico prediction tools.
Drug discovery is a complex process that requires identifying molecules capable of modulating biological targets relevant to disease. Once targets are defined, scientific teams must evaluate and prioritize thousands of potential compounds, often involving high costs and long development timelines before a candidate can move into preclinical or clinical stages.
In this context, progress in machine learning, scientific computing, and large-scale data analysis is transforming early-stage research. These approaches enable teams to prioritize compounds with a higher probability of success, helping reduce research time and focus experimental resources more efficiently.
ProtoQSAR develops computational platforms for predicting physicochemical, (eco)toxicological, and pharmacokinetic properties. Among these solutions, ProtoPRED® stands out as a QSAR-based predictive tool designed to rapidly and reliably estimate multiple parameters relevant to molecular design, supporting decision-making at early stages.
Through this agreement, IQM-CSIC will gain access to ProtoPRED® for its medicinal chemistry research lines. This will allow researchers to evaluate and prioritize molecules more efficiently, optimize the design of new chemical series, and accelerate the identification of candidates with more favorable pharmacokinetic profiles and lower toxicological risk.
The collaboration combines IQM-CSIC’s expertise in compound design, synthesis, and biological evaluation with ProtoQSAR’s capabilities in predictive modeling. By integrating experimental data and computational predictions into iterative design cycles, the partnership aims to strengthen drug discovery workflows and advance safer, more effective therapeutic solutions.
