Molecular Embedding–Based Algorithm Selection in Protein–Ligand Docking 1. A new study introduces MolAS, a lightweight algorithm selection system that predicts docking algorithm performance using molecular embeddings. This approach significantly improves docking accuracy by selecting the best algorithm for each specific protein-ligand complex. 2. MolAS leverages pretrained protein and ligand embeddings combined with a shallow residual decoder. It achieves up to 15% absolute improvement over the single-best solver (SBS) and closes 17–66% of the Virtual Best Solver (VBS)–SBS gap across diverse benchmarks. 3. The study highlights that the main barrier to robust docking algorithm selection is not representational capacity but instability in solver rankings across different docking protocols. MolAS serves as both a practical selector and a diagnostic tool for assessing algorithm selection feasibility. 4. MolAS was evaluated on multiple benchmarks, including MOAD-curated, PoseX, and PoseBusters datasets. It demonstrated strong performance improvements in cross-docking scenarios, showing its adaptability to different docking regimes. 5. The research also includes detailed analyses of embedding geometry and solver selection patterns, revealing that MolAS succeeds when the oracle landscape exhibits low entropy and separable solver behavior. However, it collapses under protocol-induced hierarchy shifts. 6. Ablation studies indicate that neither deeper encoders nor alternative objective functions provide systematic improvements, suggesting that the current model architecture is well-suited for the available data scale. 7. The findings suggest that future advancements in docking algorithm selection will require explicit modeling of workflow changes rather than further architectural scaling. This work provides a clear scope for practical applications and future research directions. 📜Paper: arxiv.org/abs/2512.02328v1 #MolecularDocking #AlgorithmSelection #ComputationalBiology #ProteinLigandInteraction #AIinBiology

Thought Database: THIS *IS #ClickBait FOR CHARLIE V* himself]. _______ In #youtubepremium madness I HAD THE MOST [for the specific #YouTuber himself] --> an 'awesome memory and mind occurrence' - *which I had made in a #YouTubeLive comment many times about* - saying where the F did this guy come from, and that being in the past. And *some of them flicked up in the reel* [must have been wildly doubly #algorithmselection anyways]. So this one and then a few *it appears almost now in temporal order* so defa #algorithmdevelopment at work... But I HONESTLY JUST WANTED *YOU TO KNOW* as IT *APPEARS BY THE COMMENTS that you do not know* ... [Aside from this - I urge people to *care less about nonsense* (this applies circumlaterally {and spherically} - and thus just for *anyone interested]. ______ Where 'summary' = anyone interested = *anyone who watches #CharlieVeitch and/or may have watched this for a while and WANTED TO KNOW* 🤔 😀 😄 😅 😉 😬 🤔..... CAUSE SURELY *AFTER HIM APPEARING THIS MANY TIMES AND IN 'CONSECUTIVE EPS' and nobody did a mini review *?? [I would have thought your fanatics themselves might have!] But, actually, the only way it was done [by me i.e.] = *eidetic memory and quick mind to eyes or other facial structures* [when placed in the mind's eye (like a collage {that you can slowly add bits to - or quickly} - but not as nice)]. _______ In other words. *****THIS IS SUMMARY***** = relates to #youtubepremiumราคาถูก #YouTube_INILIST = #LiberalismIsAMentalDisease = FHR European Ventures LLP v Cedar Capital Partners LLC #commonlaw THIS IS 'ALL WORTH IT' IF THE 'ACTUAL CHARLIE Veitch off #youtubeshorts and NOT #TikTokLIVE nonsense #TikTokviral but #YTShorts of tall dude'... #SHALOMMOVEMENT #shalomyarse [this 'could' be used as a viral #hashtag for *people who feign to care about a good cause but in reality their cause is far worse than your capacity to understand* - literally]. ______ ALSO WHAT THE FK is #Twitter posting these fkn *bot female model profiles saying 'what do you wanna do stud' - where ya gonna take a lass like me*?? [All #aivideo ]. So corrupted = any mention at all pf [inside posts *that i.e. have nothing to do with that mentioned thing except for this mentioning* and then you end up with this in your #algorithm feed* --> such as this]. #CharlesVeitch = *I am paying money so this tweet is eventually seen by you* --> I will let the computer do the work, and then when/if it is accurate [and I.e. you did NOT KNOW that grandpa was that guy = *i.e. how you had come to develop such a protracted and intense enemy*]. But honestly see my comment = because it is actually *saying he did it peaceful* [even if lame af]... _____ I think you would *ultimately believe this overall* ... youtu.be/NkJA2_vu96M?si=fwa5… ^^ Is the video.

#censorship #shadowban #algorithmselection @elonmusk élections = piège à cons

X has gotten rotten over the past 2-3 weeks. Or is that just me? @elonmusk - my entire feed is now either anti Israel or anti Trump. Honestly, I could give 2 f’s about either. Bring me back business, crypto & current events! #algorithmselection

#shadowban #censorship #algorithmselection

The #algorithmselection sucks b@lls i dont even care anymore man

#UnsolvedMysteries #Algorithm #ElonMusk #FreedomOfSpeech #algorithmselection #X

Thought Database: This is honestly pathetic #elonmuskliesdaily ... it even makes #Trump look bad... _____ The guy has in incurable form of cancer [depending on the level of metastasis]... Do you know many people *ask such kinds of things* all the time... How many people are desperate for help and ignored because they don't have #clout ... ______ You will [don't] not care about this post... but meh. You are a pretty pathetic knt for *making this known* ... ______ Whoever commented saying 'yeah it is easy for influential people/accounts' to get into contact with similar magnanimity... etc... But. The #algorithmselection ENSURES that most people are never heard... At least be 'consistent'... Imagine #RFKjr being so disingenuous as to say *the #president wants to help* ... honestly... as if #DonaldTrump has fkn time to help every man and their dog who ever had a health problem... fkn pathetic... Don't subjugate the word and time of the president by #Gaslighting people into believing he is EVEN AWARE of this *before it is handed down by 20 advisors and etc before he even hears of it* ...

Thought Database: Fuck off #CandaceOwe ...! You haven't found fucking anything...! This 'whole time' you have released video after video and tweet after tweet... about #CharlieKirkdead #CharlieKirk _______ You are JUST trying to #EngagementFarm = *Oh I will [maybe... (ye. As IF 'maybe' = DEFINITEL)] 'go #Livestream tomorrow*... = You just want #clout and attention... you are SO see through!! 😆 🤣 😂 _______ You are dense... lol, I previously wondered if you were 'more clever' than [I] gave you credit for... But. No... like a pane of glass... you *use #AI too craft your #tweets by editing it afterwards* and 'particular numbers of characters' as instructed by systemic advice on #algorithmselection ... And you are a very low IQ person... [i.e. no you are not 'even a third' as (dumb) clever as you think you are {and a 1/10th as much as people think}]... _____ 'Warned' all [although of course won't listen/pay attention] (but ironically WILL pay #candace ... {think of all those 'live' donations you give})... = say 100000 people [modest estimate {and take averages over v high 'donations' to low}] = x 2$ = 200,000USD. You are all gonna 'pay [attention] this tinhat flat eather over 300000 AUD' for a 1 hour long rant about fkn nothing... THAT IS WHAT YOU IDIOTS are paying 'this idiot' to talk about NONSENSE...! She has [to repeat] NOT 'found' ONE SINGLE thing of *any importance or actual significance* - you are all just being SCAMMED = #ScamWarning #ScamAlert by this Piccolo witch 🧙‍♀️ #GRIFTER ...! ______ She has NOT PRODUCED ONE SINGLE 'receipt' worth more than its weight in paper...! 😆 🤣 😂 _______ But, @RealCandaceO the only legitimately dark thing you have discovered is your fkn soul you hapless grifter.

#Handyman #TechGiants #techwar #algorithmselection

🚀Day 13 of #100DaysOfCode · LeetCode 1716 ✅ Let w = n // 7, r = n % 7. Total = 28*w 7*w*(w-1)/2 r*(1 w) r*(r-1)/2 — sums full weeks leftover days. O(1) time, O(1) space. Simple math beats simulation. #LeetCode #algorithmselection

What's your wildest wish? #DragonNFT #algorithmselection

MC-GNNAS-Dock: Multi-criteria GNN-based Algorithm Selection for Molecular Docking 1. A new study introduces MC-GNNAS-Dock, an enhanced system for selecting the most suitable molecular docking algorithms based on multi-criteria evaluation. This system integrates geometric accuracy, pose validity, and ranking quality to improve the consistency and reliability of docking predictions. 2. The core innovation of MC-GNNAS-Dock is its composite scoring function, which combines RMSD (root mean square deviation) for geometric accuracy with PoseBusters for pose validity. This dual evaluation ensures that the selected docking algorithm not only predicts accurate binding poses but also generates chemically feasible results. 3. The system incorporates a ranking-aware loss function, including pairwise logistic loss and NDCG loss, to optimize the selection process. This approach aligns the model's output with ordinal preferences, enhancing the model's ability to rank algorithms effectively. 4. MC-GNNAS-Dock adopts a refined model architecture with residual connections in the decoder, which significantly improves predictive robustness and performance. The residual connections support better gradient propagation and feature reuse, making the system more effective for molecular graph embeddings. 5. Extensive experiments on a diverse dataset of approximately 3200 protein-ligand complexes from PDBBind demonstrate the superior performance of MC-GNNAS-Dock. It achieves up to 5.4% gains in RMSD below 1 Å and 3.4% gains in RMSD below 2 Å with PoseBuster-validity compared to the single best solver. 6. The study also explores the scalability of MC-GNNAS-Dock to larger portfolios of docking algorithms, showing that the system can effectively exploit the performance complementarity across different methods. This scalability is crucial for handling the diversity of docking scenarios in real-world applications. 7. Future work will focus on refining the ranking-aware loss terms and exploring their parameter sensitivity. Additionally, extending the evaluation to cross-docking cases and integrating more advanced docking methods will further enhance the generalizability of MC-GNNAS-Dock. 📜Paper: arxiv.org/abs/2509.26377v1 #MolecularDocking #AlgorithmSelection #GraphNeuralNetworks #ComputationalBiology #DrugDiscovery

Reliable algorithm selection for machine learning-guided design @PrescientDesign @Genentech 1. This paper introduces a novel method for selecting machine learning-guided design algorithms that can reliably generate desired outputs meeting user-defined success criteria, such as producing protein sequences with high binding affinity or RNA sequences with stable structures. 2. The proposed approach frames design algorithm selection as a multiple hypothesis testing problem, assessing whether a configuration will produce successful outputs by combining predictions with held-out labeled data. 3. By leveraging prediction-powered inference techniques, the method effectively corrects prediction bias introduced by differences between training and labeled data distributions. It provides statistical guarantees of selecting successful configurations with high probability. 4. Unlike traditional methods, this approach focuses on population-level success rather than individual predictions, enabling it to make robust decisions about algorithm performance even when individual predictions are noisy or uncertain. 5. The paper demonstrates the approach’s efficacy on simulated protein and RNA design tasks, showing that the method maintains high selection accuracy even when density ratios between design and labeled distributions are unknown and must be estimated. 6. Experiments reveal that the proposed method significantly outperforms prediction-only and calibrated forecast methods, particularly under covariate shift scenarios where labeled data and design data distributions differ. 7. The method provides a versatile framework for deploying machine learning-based design algorithms across various domains, offering a rigorous and statistically sound approach to selecting algorithms for tasks involving protein, RNA, and other biomolecular designs. @clara_fannjiang 📜Paper: arxiv.org/abs/2503.20767 #MachineLearning #AlgorithmSelection #ProteinDesign #RNADesign #PredictionPoweredInference #ComputationalBiology #Bioinformatics

Implementing Product Recommendations on Search Engines! 🛍️ #ProductRecommendations #SearchEngines #DataIntegration #AlgorithmSelection #EcommerceSales #Personalization #PerformanceOptimization #UserPreferences #UserBehaviour #Ecommerce #Recommendation #EcommerceBusiness #BuyMore

🤯 Lowkey Goated When #AlgorithmSelection Is The Vibe 🤯 Check out @yubai01 et al.'s paper - Provable In-Context Learning with In-Context Algorithm Selection deepai.org/publication/trans…

📢Reminder: Registration deadline for the #COSEAL Workshop 2022 is today! If you are interested in topics related to #AlgorithmSelection, #AlgorithmConfiguration or #AutoML, consider to register. It's free of charge, online and you can widen your network. coseal.net/coseal-workshop-2…

Come join and listen to our talk on extreme algorithm selection! @wever_marcel #AI #ML #AlgorithmSelection

It feels great to see our paper "Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis" published! Check it out, if you haven't yet :). #AI #MachineLearning #AlgorithmSelection

Our paper "Hybrid Ranking and Regression for Algorithm Selection" which was accepted at the KI 2020 is now published: link.springer.com/chapter/10… @wever_marcel @HanselleJonas #AI #AlgorithmSelection #MachineLearning