@Andrew_Akbashev On one hand your posts are descriptive, concise and accurately reflect academic reality. On the other hand, if you spent less time writing on social media maybe you would have a wealthier publication record and would get the funding. 😁
Thrilled with my decision to subscribe to ChatGPT! 🎉Its performance surpasses Google Bard by leaps and bounds - truly an investment worth making! 🚀🌟 #AI#ChatGPT#GoogleBard https://t.co/beQAqyd7YP
@joe_fenrir@andrei_yudin In our recent preprint, we demonstrate that explicit calculation of ligand deformation energy and
ligand conformation entropy on the PM6-D3H4X/COSMO level has no positive effect on protein-ligand scoring, despite the huge computational burden they entail.
https://t.co/OuPgDgi7gR
@joe_fenrir@andrei_yudin Well, I don't know whether most of the ligands do not bind in a local minimum conformation, but what I can tell for sure is that the contribution of the strain energy and conformational entropy to ligand binding is overrated.
@Andrew_Akbashev It couldn't be more true! Postdoc salaries are not only disgraceful for the skills and efforts of the employee, but are usually barely enough for someone to live alone. Imagine having a family to support financially and children to raise with this money...🤯
Structure calculations: NOE distance restraint creation and subsequently Replica Exchange Molecular Dynamics simulations with Solute Tempering from the extended peptide conformation using MELD software. (3 days on a commodity PC with Intel Core i9-12900KF processor and A4000 GPU)
@LabSchanda In AI|ffinity we develop a new version rewritten from scratch, which is based on AI and allows whole protein resonance assignment from only one 4D spectrum. Stay tuned to learn more soon. 😉
Structure calculations: Replica Exchange Molecular Dynamics simulations with Solute Tempering using MELD software. (10 days on a commodity PC with Intel Core i9-12900KF processor and A4000 GPU)
@raghurama123 R^2 is not a good evaluation metric for free energy methods. In the D3R GC they used RMSE and affinity ranking correlation coefficients to evaluate participants' predictions. After all in lead optimization is the ranking power that matters.