Computational Chemistry

Computational Chemistry takes advantage of both fast and accurate in silico techniques to obtain insights at the molecular level, providing valuable information essential for expediting the drug discovery and development processes.

Why choose us

Here at Red Glead Discovery (RGD), we aim to approach the clients’ needs in every way possible. By routinely implementing Computational Chemistry (CC) into the drug development pipeline, we accomplish this objective in two distinct ways. The information from the initial exploitation of possible scaffold templates can be swiftly passed to the medicinal chemistry team, speeding up the development of an initial library of compounds. Alternatively, from the initial biochemical or biological results, preliminary structure-activity relationships are computed for further selection of the most promising compounds, thus saving valuable bench time and resources.

While most useful in conjunction with medicinal chemistry approaches, CC is also able to provide standalone responses, either by setting up the foundational ideas for exploratory projects or as a tool to obtain initial roadmaps for biophysical, biological, or toxicological processes.

Lastly, the medicinal chemistry background of the CC team assures a robust and prompt response to the chemistry teams by offering solutions that can be readily integrated into the current synthetic strategy,  speeding up the way science is translated from the in silico to the “real” world.

Computational Chemistry & Integrated drug discovery

RGD is committed to implementing CC as an intrinsic part of the cornerstone of our drug-discovery arsenal. Using advanced state-of-the-art in silico methodologies we aim to further accelerate the development of lead compounds into clinical candidates by generating computational models “on-the-fly”, able to guide the medicinal chemistry effort at a faster pace by avoiding pitfalls and low-activity compounds. Peptide Chemistry and ADME & Analysis services also benefit from the expanding CC capabilities such as protein folding predictions, quantitative structure-activity relationship modelling, and software implementation for initial toxicological and pharmacokinetic predictions.

Case 1

Taking advantage of the large dataset from an ongoing drug discovery project concerning the development of CNS drugs for a US Biotech company, a multi-parameter evaluation protocol was implemented. Using CC, not only was potency targeted for optimization but also passive permeation, efflux susceptibility, and cardiac toxicity. For this particular target, both 2D- or 3D-models were able to be built, mostly derived from experimental data obtained in-house. This approach provided valuable information for the design and optimization of novel chemical entities with improved potency while reducing off-target interactions.

Case 2

RGD, in collaboration with academia, is committed to the development of small molecules able to target cancer stem cells. With the aim to target and inhibit cancer progression, together with its co-proprietary technology WACTM, a set of low-molecular weight fragments were identified as strong binders to an important target that is currently undruggable. Herein, a computational-based fragment approach through Molecular Dynamics simulations provided the initial clues for all possible binding sites within the proposed target, clearly identifying the most promising drug-binding locations, thus enabling more informed planning for expansion of the initial library derived from the WACTM hits.

Our approach

Our drug development pipeline is mostly focused on thoroughly understanding structure-activity relationships that can help guide the medicinal chemistry effort. The most common workflow employs standard computational approaches such as 2D- or 3D-QSAR model generation, molecular docking, or molecular dynamics techniques. We have also enabled the possibility of designing custom scoring functions for docking procedures, especially for membrane proteins. De novo drug design and Virtual Screening techniques are also part of the standard computational routines. Per on-request basis, other techniques such as ab initio quantum calculations for reaction transition states, protein model structure evaluation, NMR chemical shift predictions, or toxicological/pharmacokinetic predictions using specialized software, are also available to fulfil the client’s needs.



We have chosen a complete set of software packages for routine drug discovery approaches, including:

  • Molecular Docking and Molecular Dynamics
  • Pharmacophore exploitation and modeling
  • De novo drug design
  • Regression models design, including Deep Learning approaches
  • QSAR model prediction software, including those from NIEHS and EU/JRC


RGD can provide nearly all of the initial CC results within 12-72 hours as it is equipped with some of the most advanced hardware for CC calculations:

  • -AMD Ryzen 9 3950X 16-Core (32 threads) CPU @ 2.2GHz, 32 GB DIMM DDR4, Pro WS X570-ACE MB and Nvidia GeForce GTX 1050 Ti GPU
  •  Intel(R) Core (TM) i9-9920X 12-core (24-thread) CPU @ 3.50GHz, 32 GB DIMM DDR4, PRIME X299-A II MB and GeForce RTX 2080 SUPER GPU