Electron density-based GPT for optimization and suggestion of host–guest binders

Here we present a machine learning model trained on electron density for the production of host–guest binders. These are read out as simplified molecular-input line-entry system (SMILES) format with >98% accuracy, enabling a complete characterization of the molecules in two dimensions. Our model generates three-dimensional representations of the electron density and electrostatic potentials of host–guest systems using a variational autoencoder, and then utilizes these representations to optimize the generation of guests via gradient descent. Finally the guests are converted to SMILES using a transformer. The successful practical application of our model to established molecular host systems, cucurbit[n]uril and metal–organic cages, resulted in the discovery of 9 previously validated guests for CB[6] and 7 unreported guests (with association constant Ka ranging from 13.5 M−1 to 5,470 M−1) and the discovery of 4 unreported guests for [Pd214]4+ (with Ka ranging from 44 M−1 to 529 M−1)

The SERRATE protein is involved in alternative splicing in <em>Arabidopsis thaliana</em>

Howalternative splicing (AS) is regulated in plants has not yet been elucidated. Previously, we have shown that the nuclear cap-binding protein complex (AtCBC) is involved in AS in Arabidopsis thaliana. Here we show that both subunits of AtCBC (AtCBP20 and AtCBP80) interact with SERRATE (AtSE), a protein involved in the microRNA biogenesis pathway. Moreover, using a high-resolution reverse transcript-ase-polymerase chain reaction AS system we have found that AtSE influences AS in a similar way to the cap-binding complex (CBC), preferentially affecting selection of 50 splice site of first introns. The AtSE protein acts in cooperation with AtCBC: many changes observed in the mutant lacking the correct SERRATE activity were common to those observed in the cbp mutants. Interestingly, significant changes in AS of some genes were also observed in other mutants of plant microRNA biogenesis pathway, hyl1-2 and dcl1-7, but a majority of them did not cor-respond to the changes observed in the se-1mutant. Thus, the role of SERRATE in AS regulation is distinct from that of HYL1andDCL1, and is similar to the regu-lation of AS in which CBC is involved

Electron density-based GPT for optimization and suggestion of host-guest binders

Here we present a machine learning model trained on electron density for the production of host-guest binders. These are read out as simplified molecular-input line-entry system (SMILES) format with &gt;98% accuracy, enabling a complete characterization of the molecules in two dimensions. Our model generates three-dimensional representations of the electron density and electrostatic potentials of host-guest systems using a variational autoencoder, and then utilizes these representations to optimize the generation of guests via gradient descent. Finally the guests are converted to SMILES using a transformer. The successful practical application of our model to established molecular host systems, cucurbit[n]uril and metal-organic cages, resulted in the discovery of 9 previously validated guests for CB[6] and 7 unreported guests (with association constant K a ranging from 13.5 M -1 to 5,470 M -1) and the discovery of 4 unreported guests for [Pd 21 4] 4+ (with K a ranging from 44 M -1 to 529 M -1). </p